二分类排斥子和鞍点的准确率的表达式pa
|
r1 |
r2 |
||
|
<1 |
<1 |
吸引子 |
c |
|
>1 |
>1 |
排斥子 |
p |
|
>1 |
<1 |
鞍点 |
a |
|
<1 |
>1 |
反鞍点 |
fa |
本文制作一个二分类网络用来分类p和a，通过改变测试集p和a的比例观察网路分类能力的变化，并将得到的数据拟合成数学表达式。
训练集的比例p：a=1：1，
测试集p和a的比例为x：y.
让x：y的比例分别为0：10，1：9，2：8，3：7，4：6，5：5，6：4，7：3，8：2，9：1，10：0。
实验过程
二分类排斥子和鞍点
制作一个4*4*2的网络向这个的左侧输入排斥子p，并让左侧网络向1，0收敛；向右侧网络输入鞍点a让右侧向0，1收敛，并让4*4*2部分权重共享，前面大量实验表明这种效果相当于将两个弹性系数为k1，k2的弹簧并联成一个弹性系数为k的弹簧，并且让k1=k2=k/2的过程。
这个网络的收敛标准是
if (Math.abs(f2[0]-y[0])< δ && Math.abs(f2[1]-y[1])< δ )
因为对应每个收敛标准δ都有一个特征的迭代次数n与之对应因此可以用迭代次数曲线n(δ)来评价网络性能。
本文尝试了δ从0.5到1e-6在内的36个值.
|
具体进样顺序 |
|||
|
进样顺序 |
迭代次数 |
||
|
δ=0.5 |
|||
|
P |
1 |
判断是否达到收敛 |
|
|
a |
2 |
判断是否达到收敛 |
|
|
梯度下降 |
|||
|
P |
3 |
判断是否达到收敛 |
|
|
a |
4 |
判断是否达到收敛 |
|
|
梯度下降 |
|||
|
…… |
|||
|
达到收敛标准测量准确率,记录迭代次数，将这个过程重复199次 |
|||
|
δ=0.4 |
|||
|
… |
|||
|
δ=1e-6 |
|||
将这个网络简写成
d2（p，a）-4-4-2-（2*k）,k∈{0,1}
排斥子的初始化方式
Random rand1 =new Random();
int ti1=rand1.nextInt(99)+1;
x[0]=sig(1+ ((double)ti1/100) );
Random rand2 =new Random();
int ti2=rand2.nextInt(99)+1;
x[3]=sig ( 1+((double)ti2/100) );
鞍点的初始化方式
Random rand1 =new Random();
int ti1=rand1.nextInt(99)+1;
x[0]=sig(1+ ((double)ti1/100) );
Random rand2 =new Random();
int ti2=rand2.nextInt(99)+1;
x[3]=sig ( ((double)ti2/100) );
得到的数据
以第一组为例
|
训练集 |
0.5 |
p |
||||||
|
0.5 |
a |
|||||||
|
测试集 |
0 |
p |
||||||
|
1 |
a |
|||||||
|
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 |
最大准确率p-max |
|
0.514954 |
0.499634 |
178.9899 |
0 |
0.5 |
2.226131 |
444 |
0.0074 |
0 |
|
0.415923 |
0.588426 |
3992.095 |
0.876017 |
0.4 |
9.457286 |
1883 |
0.031383 |
0.951952 |
|
0.285787 |
0.717031 |
4830.568 |
0.899497 |
0.3 |
10 |
1992 |
0.0332 |
0.964965 |
|
0.185407 |
0.819002 |
5277.367 |
0.911172 |
0.2 |
10.07538 |
2006 |
0.033433 |
0.986987 |
|
0.089429 |
0.911647 |
6136.231 |
0.916952 |
0.1 |
11.36181 |
2261 |
0.037683 |
0.975976 |
|
0.008561 |
0.991586 |
8374.784 |
0.929714 |
0.01 |
15.9799 |
3180 |
0.053 |
0.981982 |
|
8.58E-04 |
0.999156 |
11611.27 |
0.943104 |
0.001 |
20.94975 |
4169 |
0.069483 |
0.988989 |
|
8.50E-05 |
0.999915 |
17998.72 |
0.955176 |
1.00E-04 |
30.72362 |
6114 |
0.1019 |
0.994995 |
|
7.79E-05 |
0.999922 |
18298.15 |
0.957319 |
9.00E-05 |
31.28141 |
6225 |
0.10375 |
0.993994 |
|
6.89E-05 |
0.999932 |
18879.48 |
0.955689 |
8.00E-05 |
32.14573 |
6412 |
0.106867 |
0.991992 |
|
6.06E-05 |
0.99994 |
19210.73 |
0.954809 |
7.00E-05 |
33.1407 |
6595 |
0.109917 |
0.992993 |
|
5.15E-05 |
0.999949 |
19577.49 |
0.956263 |
6.00E-05 |
33.39698 |
6648 |
0.1108 |
0.995996 |
|
4.33E-05 |
0.999957 |
20861.04 |
0.956414 |
5.00E-05 |
36.85427 |
7335 |
0.12225 |
0.998999 |
|
3.46E-05 |
0.999966 |
21738.86 |
0.955815 |
4.00E-05 |
37.68342 |
7499 |
0.124983 |
0.992993 |
|
2.59E-05 |
0.999974 |
22956.84 |
0.956001 |
3.00E-05 |
39.17085 |
7810 |
0.130167 |
0.994995 |
|
1.72E-05 |
0.999983 |
25275.3 |
0.957319 |
2.00E-05 |
43.54271 |
8665 |
0.144417 |
0.98999 |
|
8.63E-06 |
0.999991 |
29969.81 |
0.959698 |
1.00E-05 |
51.23618 |
10196 |
0.169933 |
0.991992 |
|
7.73E-06 |
0.999992 |
30247.04 |
0.957556 |
9.00E-06 |
51.62312 |
10274 |
0.171233 |
0.993994 |
|
6.84E-06 |
0.999993 |
30677.38 |
0.958536 |
8.00E-06 |
52.26633 |
10401 |
0.17335 |
0.988989 |
|
6.02E-06 |
0.999994 |
31932.25 |
0.958884 |
7.00E-06 |
55.18593 |
10997 |
0.183283 |
0.991992 |
|
5.20E-06 |
0.999995 |
33557.89 |
0.959105 |
6.00E-06 |
57.37186 |
11433 |
0.19055 |
0.995996 |
|
4.32E-06 |
0.999996 |
33544.95 |
0.959467 |
5.00E-06 |
57.32663 |
11409 |
0.19015 |
0.990991 |
|
3.42E-06 |
0.999997 |
35471.9 |
0.961368 |
4.00E-06 |
60.07035 |
11954 |
0.199233 |
0.997998 |
|
2.56E-06 |
0.999997 |
38427.08 |
0.95912 |
3.00E-06 |
65.20101 |
12975 |
0.21625 |
0.994995 |
|
1.70E-06 |
0.999998 |
41693.27 |
0.959955 |
2.00E-06 |
71.00503 |
14146 |
0.235767 |
0.991992 |
|
8.60E-07 |
0.999999 |
49787.77 |
0.959155 |
1.00E-06 |
85.94472 |
17103 |
0.28505 |
0.991992 |
这个网络的测试集p与a的比例是0：1，也就全是a。
平均准确率p-ave，是199次收敛的平均值
最大准确率p-max，是199次收敛的最大值
将得到的平均准确率画成图
可见网络的平均准确率没有随着迭代的增加而增加而是趋于一个常数，将1e-5到1e-6的平均准确率平均为0.959284。
也就是在训练集p：a=1：1的情况下如果测试集p：a=0：1网络可以接近恒定正确的区分出95.9284%的a。
其他的数据
|
a |
1 |
0.9 |
0.8 |
0.7 |
0.6 |
0.5 |
0.4 |
0.3 |
0.2 |
0.1 |
0 |
|
|
平均准确率p-ave |
||||||||||||
|
p |
δ |
0 |
0.1 |
0.2 |
0.3 |
0.4 |
0.5 |
0.6 |
0.7 |
0.8 |
0.9 |
1 |
|
0.5 |
0 |
0.101202 |
0.199697 |
0.29948 |
0.402669 |
0.501768 |
0.599791 |
0.700816 |
0.799377 |
0.900126 |
1 |
|
|
0.4 |
0.876017 |
0.883587 |
0.900599 |
0.910423 |
0.919412 |
0.932903 |
0.946625 |
0.953763 |
0.967928 |
0.978853 |
0.990277 |
|
|
0.3 |
0.899497 |
0.908054 |
0.918833 |
0.921112 |
0.928144 |
0.936147 |
0.942691 |
0.948823 |
0.956036 |
0.963396 |
0.969366 |
|
|
0.2 |
0.911172 |
0.914271 |
0.918848 |
0.923667 |
0.929392 |
0.93247 |
0.936565 |
0.940554 |
0.94664 |
0.949195 |
0.956092 |
|
|
0.1 |
0.916952 |
0.919437 |
0.921152 |
0.923713 |
0.926494 |
0.928758 |
0.930322 |
0.93325 |
0.936097 |
0.936132 |
0.938154 |
|
|
0.01 |
0.929714 |
0.933305 |
0.930363 |
0.926343 |
0.923622 |
0.92085 |
0.91823 |
0.914553 |
0.910252 |
0.913813 |
0.905795 |
|
|
0.001 |
0.943104 |
0.939648 |
0.932847 |
0.928411 |
0.919684 |
0.914462 |
0.906615 |
0.903748 |
0.895654 |
0.88993 |
0.884875 |
|
|
1.00E-04 |
0.955176 |
0.946504 |
0.937043 |
0.92917 |
0.921137 |
0.909729 |
0.904699 |
0.893582 |
0.884543 |
0.875262 |
0.862712 |
|
|
9.00E-05 |
0.957319 |
0.943386 |
0.937329 |
0.927656 |
0.91997 |
0.910896 |
0.90159 |
0.890292 |
0.888235 |
0.873024 |
0.871288 |
|
|
8.00E-05 |
0.955689 |
0.944869 |
0.937249 |
0.928366 |
0.919352 |
0.911746 |
0.900599 |
0.892928 |
0.882541 |
0.874754 |
0.863804 |
|
|
7.00E-05 |
0.954809 |
0.944512 |
0.936892 |
0.928708 |
0.919236 |
0.909608 |
0.901826 |
0.891102 |
0.887657 |
0.874221 |
0.868844 |
|
|
6.00E-05 |
0.956263 |
0.945876 |
0.938164 |
0.928008 |
0.920639 |
0.910071 |
0.900398 |
0.895443 |
0.885649 |
0.876298 |
0.866203 |
|
|
5.00E-05 |
0.956414 |
0.948868 |
0.936177 |
0.927782 |
0.91994 |
0.909015 |
0.901369 |
0.88912 |
0.881374 |
0.869221 |
0.863632 |
|
|
4.00E-05 |
0.955815 |
0.947148 |
0.937354 |
0.929422 |
0.920282 |
0.910735 |
0.901872 |
0.892546 |
0.884291 |
0.872777 |
0.866319 |
|
|
3.00E-05 |
0.956001 |
0.94741 |
0.938516 |
0.929533 |
0.919427 |
0.910383 |
0.901671 |
0.889533 |
0.88153 |
0.868622 |
0.861138 |
|
|
2.00E-05 |
0.957319 |
0.949688 |
0.939729 |
0.929507 |
0.919824 |
0.911313 |
0.899316 |
0.888703 |
0.882999 |
0.869679 |
0.867254 |
|
|
1.00E-05 |
0.959698 |
0.94669 |
0.938733 |
0.92908 |
0.91999 |
0.911504 |
0.90076 |
0.890841 |
0.885172 |
0.873054 |
0.864281 |
|
|
9.00E-06 |
0.957556 |
0.947938 |
0.939628 |
0.929754 |
0.920911 |
0.909965 |
0.900011 |
0.892118 |
0.878351 |
0.869654 |
0.863567 |
|
|
8.00E-06 |
0.958536 |
0.949155 |
0.938728 |
0.930247 |
0.921067 |
0.911711 |
0.899377 |
0.892888 |
0.881681 |
0.873034 |
0.865152 |
|
|
7.00E-06 |
0.958884 |
0.950498 |
0.939301 |
0.931037 |
0.919497 |
0.911439 |
0.902656 |
0.889694 |
0.880046 |
0.876751 |
0.866404 |
|
|
6.00E-06 |
0.959105 |
0.947898 |
0.940881 |
0.930222 |
0.91913 |
0.911681 |
0.901328 |
0.892023 |
0.882948 |
0.873089 |
0.859528 |
|
|
5.00E-06 |
0.959467 |
0.948179 |
0.939236 |
0.93078 |
0.92074 |
0.911012 |
0.901359 |
0.892878 |
0.888557 |
0.87319 |
0.862541 |
|
|
4.00E-06 |
0.961368 |
0.948662 |
0.941072 |
0.929985 |
0.92071 |
0.911821 |
0.901826 |
0.892586 |
0.881811 |
0.870805 |
0.863758 |
|
|
3.00E-06 |
0.95912 |
0.949266 |
0.939854 |
0.931751 |
0.922566 |
0.910302 |
0.901258 |
0.890665 |
0.884422 |
0.871077 |
0.867184 |
|
|
2.00E-06 |
0.959955 |
0.950131 |
0.941026 |
0.930795 |
0.923617 |
0.912631 |
0.903401 |
0.895061 |
0.880634 |
0.874804 |
0.862033 |
|
|
1.00E-06 |
0.959155 |
0.951977 |
0.942078 |
0.931872 |
0.922495 |
0.912908 |
0.904553 |
0.895856 |
0.888783 |
0.875519 |
0.86898 |
|
|
0.959284 |
0.949039 |
0.940054 |
0.930552 |
0.921072 |
0.911497 |
0.901653 |
0.892461 |
0.883241 |
0.873098 |
0.864343 |
||
也就是在训练集p：a=1：1的情况下如果测试集p：a=1：0网络可以接近恒定正确的区分出86.4343%的p。
将最后一行准确率的平均值画成图是一条直线
如果用pp和pa表示测试集中p与a的比例则
d2（p，a）-4-4-2-（2*k）,k∈{0,1}的平均准确率的表达式为
|
平均准确率p-ave |
||||||||||||
|
pa |
1 |
0.9 |
0.8 |
0.7 |
0.6 |
0.5 |
0.4 |
0.3 |
0.2 |
0.1 |
0 |
|
|
pp |
δ |
0 |
0.1 |
0.2 |
0.3 |
0.4 |
0.5 |
0.6 |
0.7 |
0.8 |
0.9 |
1 |
|
实测值 |
0.959284 |
0.949039 |
0.940054 |
0.930552 |
0.921072 |
0.911497 |
0.901653 |
0.892461 |
0.883241 |
0.873098 |
0.864343 |
|
|
计算值0.959284*pa+0.864343*pp |
0.959284 |
0.94979 |
0.940296 |
0.930802 |
0.921308 |
0.911814 |
0.902319 |
0.892825 |
0.883331 |
0.873837 |
0.864343 |
|
将实测值和计算值画成图
图像是高度重合的。
实验参数
|
学习率 0.1 |
|
权重初始化方式 |
|
Random rand1 =new Random(); |
|
int ti1=rand1.nextInt(98)+1; |
|
tw[a][b]=xx*((double)ti1/100); |
|
迭代次数n |
|||||||||||
|
δ |
0 |
0.1 |
0.2 |
0.3 |
0.4 |
0.5 |
0.6 |
0.7 |
0.8 |
0.9 |
1 |
|
0.5 |
178.9899 |
175.2211 |
175.6633 |
176.7286 |
174.9698 |
175.2814 |
176.5377 |
177.4121 |
174.9196 |
170.8995 |
178.8392 |
|
0.4 |
3992.095 |
4101.196 |
4076.523 |
4155.457 |
4006.583 |
4086.05 |
4030.116 |
4109.563 |
4115.342 |
4017.347 |
4051.658 |
|
0.3 |
4830.568 |
4713.518 |
4719.558 |
4833.392 |
4784.146 |
4760.03 |
4862.226 |
4740.181 |
4760.95 |
4721.533 |
4796.181 |
|
0.2 |
5277.367 |
5272.432 |
5314.251 |
5231.136 |
5421.779 |
5363.698 |
5488.477 |
5407.196 |
5349.799 |
5316.603 |
5451.658 |
|
0.1 |
6136.231 |
5991.387 |
6012.482 |
6147.387 |
6021.839 |
6068.291 |
6023.296 |
6141.548 |
5938.312 |
6031.166 |
6081.799 |
|
0.01 |
8374.784 |
8274.935 |
8297.186 |
8344.181 |
8307.709 |
8312.714 |
8373.266 |
8323.095 |
8314.472 |
8184.02 |
8227.578 |
|
0.001 |
11611.27 |
11731.6 |
11651.79 |
11758.99 |
11852.64 |
11708.52 |
11769.97 |
11895.17 |
11630.32 |
11934.5 |
11658.61 |
|
1.00E-04 |
17998.72 |
17924.94 |
18386.23 |
17823.12 |
17950.76 |
18032.69 |
18349.92 |
18039.44 |
17969.95 |
18146.08 |
18020.46 |
|
9.00E-05 |
18298.15 |
18370.07 |
18272.76 |
18671.64 |
18371.74 |
18455.81 |
18306.11 |
18449.29 |
18655.23 |
18287.8 |
18260.02 |
|
8.00E-05 |
18879.48 |
18877.53 |
18968.96 |
18814.26 |
19029.53 |
18907.17 |
18780.89 |
18454.62 |
18774.82 |
18842.83 |
19365.37 |
|
7.00E-05 |
19210.73 |
19618.94 |
19058.27 |
18864.31 |
19699.54 |
19192.85 |
19353.83 |
19463.43 |
19908.6 |
19146.66 |
19051.94 |
|
6.00E-05 |
19577.49 |
20321.44 |
19950.06 |
20230.75 |
20016.31 |
19637.51 |
20324.79 |
20284.9 |
19755.79 |
19900.13 |
20186.96 |
|
5.00E-05 |
20861.04 |
20918.89 |
21342.23 |
20212.31 |
20355.08 |
21041.91 |
20345.64 |
20506.18 |
20452 |
20773.77 |
21040.99 |
|
4.00E-05 |
21738.86 |
21696.41 |
22189.29 |
21664.93 |
21334.22 |
21458.21 |
21949.56 |
21906.86 |
22221.49 |
21510.16 |
22071.54 |
|
3.00E-05 |
22956.84 |
23914.19 |
23013.26 |
23628.01 |
23121.98 |
23419.3 |
22980.68 |
23162.81 |
23015.72 |
22580.6 |
23027.16 |
|
2.00E-05 |
25275.3 |
25512.2 |
25625.51 |
24909.99 |
25287.12 |
25467.01 |
25387.27 |
25386.75 |
25443.69 |
25225.02 |
25235.65 |
|
1.00E-05 |
29969.81 |
29470.97 |
29902.99 |
28973.61 |
29675.12 |
28929.45 |
30018.27 |
29183.94 |
29146.75 |
29139.24 |
29858.89 |
|
9.00E-06 |
30247.04 |
30513.24 |
30112.32 |
29809.58 |
30146.41 |
30596.92 |
30042.71 |
29992.44 |
30192.51 |
29208.69 |
29416.11 |
|
8.00E-06 |
30677.38 |
31836.22 |
31413.26 |
30242.05 |
31306.97 |
31467.45 |
31193.98 |
30287.49 |
31619.56 |
30228.18 |
30564.27 |
|
7.00E-06 |
31932.25 |
30926.25 |
32104.82 |
32418.75 |
31242.93 |
32308.31 |
32049.32 |
31156.3 |
31521.76 |
31077.35 |
31670.24 |
|
6.00E-06 |
33557.89 |
33163.54 |
32469.63 |
33315.13 |
32843.59 |
33142.06 |
33314.14 |
33318.47 |
32457.08 |
32820.7 |
32734.45 |
|
5.00E-06 |
33544.95 |
32903.76 |
34470.5 |
34408.14 |
34360.62 |
34622.05 |
33135.32 |
35836.14 |
34892.76 |
34875.83 |
33449.26 |
|
4.00E-06 |
35471.9 |
36459.97 |
35645.53 |
36694.53 |
36231.65 |
37457.67 |
34921.27 |
36361.22 |
36031.88 |
36771.91 |
35864.78 |
|
3.00E-06 |
38427.08 |
38564.55 |
38165.17 |
38486.48 |
38178.81 |
39145.41 |
37738.74 |
38041.16 |
38541.94 |
38024.93 |
38843.03 |
|
2.00E-06 |
41693.27 |
40956.78 |
42375.36 |
41608.88 |
42350.46 |
41515.22 |
42446.08 |
41794.77 |
41538.48 |
42297.98 |
41138.88 |
|
1.00E-06 |
49787.77 |
48948.52 |
49260.11 |
48418.2 |
48665.55 |
48841.56 |
46846.71 |
48042.7 |
50248.82 |
49040.7 |
49087.97 |
|
训练集 |
0.5 |
p |
||||||
|
0.5 |
a |
|||||||
|
测试集 |
0 |
p |
||||||
|
1 |
a |
|||||||
|
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 |
最大准确率p-max |
|
0.514954 |
0.499634 |
178.9899 |
0 |
0.5 |
2.226131 |
444 |
0.0074 |
0 |
|
0.415923 |
0.588426 |
3992.095 |
0.876017 |
0.4 |
9.457286 |
1883 |
0.031383 |
0.951952 |
|
0.285787 |
0.717031 |
4830.568 |
0.899497 |
0.3 |
10 |
1992 |
0.0332 |
0.964965 |
|
0.185407 |
0.819002 |
5277.367 |
0.911172 |
0.2 |
10.07538 |
2006 |
0.033433 |
0.986987 |
|
0.089429 |
0.911647 |
6136.231 |
0.916952 |
0.1 |
11.36181 |
2261 |
0.037683 |
0.975976 |
|
0.008561 |
0.991586 |
8374.784 |
0.929714 |
0.01 |
15.9799 |
3180 |
0.053 |
0.981982 |
|
8.58E-04 |
0.999156 |
11611.27 |
0.943104 |
0.001 |
20.94975 |
4169 |
0.069483 |
0.988989 |
|
8.50E-05 |
0.999915 |
17998.72 |
0.955176 |
1.00E-04 |
30.72362 |
6114 |
0.1019 |
0.994995 |
|
7.79E-05 |
0.999922 |
18298.15 |
0.957319 |
9.00E-05 |
31.28141 |
6225 |
0.10375 |
0.993994 |
|
6.89E-05 |
0.999932 |
18879.48 |
0.955689 |
8.00E-05 |
32.14573 |
6412 |
0.106867 |
0.991992 |
|
6.06E-05 |
0.99994 |
19210.73 |
0.954809 |
7.00E-05 |
33.1407 |
6595 |
0.109917 |
0.992993 |
|
5.15E-05 |
0.999949 |
19577.49 |
0.956263 |
6.00E-05 |
33.39698 |
6648 |
0.1108 |
0.995996 |
|
4.33E-05 |
0.999957 |
20861.04 |
0.956414 |
5.00E-05 |
36.85427 |
7335 |
0.12225 |
0.998999 |
|
3.46E-05 |
0.999966 |
21738.86 |
0.955815 |
4.00E-05 |
37.68342 |
7499 |
0.124983 |
0.992993 |
|
2.59E-05 |
0.999974 |
22956.84 |
0.956001 |
3.00E-05 |
39.17085 |
7810 |
0.130167 |
0.994995 |
|
1.72E-05 |
0.999983 |
25275.3 |
0.957319 |
2.00E-05 |
43.54271 |
8665 |
0.144417 |
0.98999 |
|
8.63E-06 |
0.999991 |
29969.81 |
0.959698 |
1.00E-05 |
51.23618 |
10196 |
0.169933 |
0.991992 |
|
7.73E-06 |
0.999992 |
30247.04 |
0.957556 |
9.00E-06 |
51.62312 |
10274 |
0.171233 |
0.993994 |
|
6.84E-06 |
0.999993 |
30677.38 |
0.958536 |
8.00E-06 |
52.26633 |
10401 |
0.17335 |
0.988989 |
|
6.02E-06 |
0.999994 |
31932.25 |
0.958884 |
7.00E-06 |
55.18593 |
10997 |
0.183283 |
0.991992 |
|
5.20E-06 |
0.999995 |
33557.89 |
0.959105 |
6.00E-06 |
57.37186 |
11433 |
0.19055 |
0.995996 |
|
4.32E-06 |
0.999996 |
33544.95 |
0.959467 |
5.00E-06 |
57.32663 |
11409 |
0.19015 |
0.990991 |
|
3.42E-06 |
0.999997 |
35471.9 |
0.961368 |
4.00E-06 |
60.07035 |
11954 |
0.199233 |
0.997998 |
|
2.56E-06 |
0.999997 |
38427.08 |
0.95912 |
3.00E-06 |
65.20101 |
12975 |
0.21625 |
0.994995 |
|
1.70E-06 |
0.999998 |
41693.27 |
0.959955 |
2.00E-06 |
71.00503 |
14146 |
0.235767 |
0.991992 |
|
8.60E-07 |
0.999999 |
49787.77 |
0.959155 |
1.00E-06 |
85.94472 |
17103 |
0.28505 |
0.991992 |
|
训练集 |
0.5 |
p |
||||||
|
0.5 |
a |
|||||||
|
测试集 |
0.1 |
p |
||||||
|
0.9 |
a |
|||||||
|
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 |
最大准确率p-max |
|
0.514946 |
0.499667 |
175.2211 |
0.101202 |
0.5 |
2.060302 |
412 |
0.006867 |
0.127127 |
|
0.412703 |
0.591256 |
4101.196 |
0.883587 |
0.4 |
9.417085 |
1906 |
0.031767 |
0.974975 |
|
0.285532 |
0.718176 |
4713.518 |
0.908054 |
0.3 |
8.849246 |
1792 |
0.029867 |
0.955956 |
|
0.184483 |
0.818452 |
5272.432 |
0.914271 |
0.2 |
10.0603 |
2003 |
0.033383 |
0.964965 |
|
0.089488 |
0.91179 |
5991.387 |
0.919437 |
0.1 |
11.44221 |
2277 |
0.03795 |
0.962963 |
|
0.008507 |
0.99162 |
8274.935 |
0.933305 |
0.01 |
16.43719 |
3286 |
0.054767 |
0.966967 |
|
8.55E-04 |
0.999162 |
11731.6 |
0.939648 |
0.001 |
21.16583 |
4230 |
0.0705 |
0.971972 |
|
8.48E-05 |
0.999916 |
17924.94 |
0.946504 |
1.00E-04 |
30.92462 |
6155 |
0.102583 |
0.972973 |
|
7.76E-05 |
0.999923 |
18370.07 |
0.943386 |
9.00E-05 |
32.36683 |
6441 |
0.10735 |
0.970971 |
|
6.94E-05 |
0.999931 |
18877.53 |
0.944869 |
8.00E-05 |
32.68342 |
6505 |
0.108417 |
0.973974 |
|
6.02E-05 |
0.999941 |
19618.94 |
0.944512 |
7.00E-05 |
34.26131 |
6850 |
0.114167 |
0.96997 |
|
5.23E-05 |
0.999948 |
20321.44 |
0.945876 |
6.00E-05 |
35.48744 |
7063 |
0.117717 |
0.970971 |
|
4.24E-05 |
0.999958 |
20918.89 |
0.948868 |
5.00E-05 |
35.77387 |
7119 |
0.11865 |
0.96997 |
|
3.45E-05 |
0.999966 |
21696.41 |
0.947148 |
4.00E-05 |
37.32663 |
7428 |
0.1238 |
0.97998 |
|
2.57E-05 |
0.999975 |
23914.19 |
0.94741 |
3.00E-05 |
41.29146 |
8217 |
0.13695 |
0.974975 |
|
1.70E-05 |
0.999983 |
25512.2 |
0.949688 |
2.00E-05 |
43.47236 |
8683 |
0.144717 |
0.971972 |
|
8.66E-06 |
0.999991 |
29470.97 |
0.94669 |
1.00E-05 |
50.72362 |
10094 |
0.168233 |
0.970971 |
|
7.83E-06 |
0.999992 |
30513.24 |
0.947938 |
9.00E-06 |
52.77387 |
10502 |
0.175033 |
0.971972 |
|
7.00E-06 |
0.999993 |
31836.22 |
0.949155 |
8.00E-06 |
55.95477 |
11135 |
0.185583 |
0.974975 |
|
6.03E-06 |
0.999994 |
30926.25 |
0.950498 |
7.00E-06 |
53.46734 |
10655 |
0.177583 |
0.974975 |
|
5.20E-06 |
0.999995 |
33163.54 |
0.947898 |
6.00E-06 |
56.86935 |
11319 |
0.18865 |
0.978979 |
|
4.29E-06 |
0.999996 |
32903.76 |
0.948179 |
5.00E-06 |
56.14573 |
11173 |
0.186217 |
0.974975 |
|
3.46E-06 |
0.999997 |
36459.97 |
0.948662 |
4.00E-06 |
62.64824 |
12467 |
0.207783 |
0.976977 |
|
2.61E-06 |
0.999997 |
38564.55 |
0.949266 |
3.00E-06 |
66.58794 |
13252 |
0.220867 |
0.973974 |
|
1.71E-06 |
0.999998 |
40956.78 |
0.950131 |
2.00E-06 |
69.95477 |
13923 |
0.23205 |
0.976977 |
|
8.55E-07 |
0.999999 |
48948.52 |
0.951977 |
1.00E-06 |
83.75879 |
16683 |
0.27805 |
0.976977 |
|
训练集 |
0.5 |
p |
||||||
|
0.5 |
a |
|||||||
|
测试集 |
0.2 |
p |
||||||
|
0.8 |
a |
|||||||
|
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 |
最大准确率p-max |
|
0.515768 |
0.499665 |
175.6633 |
0.199697 |
0.5 |
1.819095 |
409 |
0.006817 |
0.241241 |
|
0.405578 |
0.598913 |
4076.523 |
0.900599 |
0.4 |
9.452261 |
1898 |
0.031633 |
0.955956 |
|
0.285142 |
0.71966 |
4719.558 |
0.918833 |
0.3 |
9.331658 |
1857 |
0.03095 |
0.960961 |
|
0.18482 |
0.819326 |
5314.251 |
0.918848 |
0.2 |
10.21608 |
2033 |
0.033883 |
0.965966 |
|
0.089624 |
0.913356 |
6012.482 |
0.921152 |
0.1 |
11.44221 |
2277 |
0.03795 |
0.954955 |
|
0.008701 |
0.991542 |
8297.186 |
0.930363 |
0.01 |
16.62312 |
3308 |
0.055133 |
0.956957 |
|
8.51E-04 |
0.999157 |
11651.79 |
0.932847 |
0.001 |
20.77387 |
4134 |
0.0689 |
0.957958 |
|
8.60E-05 |
0.999915 |
18386.23 |
0.937043 |
1.00E-04 |
32.02513 |
6391 |
0.106517 |
0.95996 |
|
7.88E-05 |
0.999922 |
18272.76 |
0.937329 |
9.00E-05 |
31.78894 |
6328 |
0.105467 |
0.957958 |
|
7.00E-05 |
0.999931 |
18968.96 |
0.937249 |
8.00E-05 |
33.07035 |
6582 |
0.1097 |
0.960961 |
|
5.96E-05 |
0.999941 |
19058.27 |
0.936892 |
7.00E-05 |
33.44724 |
6672 |
0.1112 |
0.95996 |
|
5.22E-05 |
0.999948 |
19950.06 |
0.938164 |
6.00E-05 |
35.60302 |
7086 |
0.1181 |
0.95996 |
|
4.33E-05 |
0.999957 |
21342.23 |
0.936177 |
5.00E-05 |
37.52764 |
7484 |
0.124733 |
0.960961 |
|
3.44E-05 |
0.999966 |
22189.29 |
0.937354 |
4.00E-05 |
38.31658 |
7625 |
0.127083 |
0.963964 |
|
2.63E-05 |
0.999974 |
23013.26 |
0.938516 |
3.00E-05 |
39.90452 |
7942 |
0.132367 |
0.962963 |
|
1.72E-05 |
0.999983 |
25625.51 |
0.939729 |
2.00E-05 |
44.37186 |
8831 |
0.147183 |
0.95996 |
|
8.58E-06 |
0.999991 |
29902.99 |
0.938733 |
1.00E-05 |
51.89447 |
10329 |
0.17215 |
0.95996 |
|
7.72E-06 |
0.999992 |
30112.32 |
0.939628 |
9.00E-06 |
52.46231 |
10441 |
0.174017 |
0.961962 |
|
6.87E-06 |
0.999993 |
31413.26 |
0.938728 |
8.00E-06 |
56.63317 |
11271 |
0.18785 |
0.961962 |
|
6.03E-06 |
0.999994 |
32104.82 |
0.939301 |
7.00E-06 |
54.90452 |
10962 |
0.1827 |
0.960961 |
|
5.01E-06 |
0.999995 |
32469.63 |
0.940881 |
6.00E-06 |
54.08543 |
10763 |
0.179383 |
0.965966 |
|
4.25E-06 |
0.999996 |
34470.5 |
0.939236 |
5.00E-06 |
57.83417 |
11509 |
0.191817 |
0.965966 |
|
3.40E-06 |
0.999997 |
35645.53 |
0.941072 |
4.00E-06 |
60.23116 |
11986 |
0.199767 |
0.963964 |
|
2.60E-06 |
0.999997 |
38165.17 |
0.939854 |
3.00E-06 |
64.25628 |
12787 |
0.213117 |
0.961962 |
|
1.71E-06 |
0.999998 |
42375.36 |
0.941026 |
2.00E-06 |
72.03015 |
14349 |
0.23915 |
0.958959 |
|
8.69E-07 |
0.999999 |
49260.11 |
0.942078 |
1.00E-06 |
82.36683 |
16391 |
0.273183 |
0.962963 |
|
训练集 |
0.5 |
p |
||||||
|
0.5 |
a |
|||||||
|
测试集 |
0.3 |
p |
||||||
|
0.7 |
a |
|||||||
|
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 |
最大准确率p-max |
|
0.515474 |
0.499642 |
176.7286 |
0.29948 |
0.5 |
1.959799 |
422 |
0.007033 |
0.335335 |
|
0.402981 |
0.602061 |
4155.457 |
0.910423 |
0.4 |
9.035176 |
1798 |
0.029967 |
0.965966 |
|
0.288652 |
0.717348 |
4833.392 |
0.921112 |
0.3 |
9.201005 |
1831 |
0.030517 |
0.956957 |
|
0.185627 |
0.819707 |
5231.136 |
0.923667 |
0.2 |
10.07035 |
2005 |
0.033417 |
0.956957 |
|
0.089602 |
0.912096 |
6147.387 |
0.923713 |
0.1 |
11.65829 |
2320 |
0.038667 |
0.953954 |
|
0.008618 |
0.991644 |
8344.181 |
0.926343 |
0.01 |
15.45226 |
3090 |
0.0515 |
0.950951 |
|
8.50E-04 |
0.999161 |
11758.99 |
0.928411 |
0.001 |
20.22613 |
4025 |
0.067083 |
0.952953 |
|
8.45E-05 |
0.999916 |
17823.12 |
0.92917 |
1.00E-04 |
30.46734 |
6063 |
0.10105 |
0.948949 |
|
7.67E-05 |
0.999925 |
18671.64 |
0.927656 |
9.00E-05 |
33.24121 |
6616 |
0.110267 |
0.94995 |
|
6.92E-05 |
0.999931 |
18814.26 |
0.928366 |
8.00E-05 |
33.58794 |
6685 |
0.111417 |
0.94995 |
|
6.02E-05 |
0.99994 |
18864.31 |
0.928708 |
7.00E-05 |
33.33668 |
6649 |
0.110817 |
0.954955 |
|
5.12E-05 |
0.999949 |
20230.75 |
0.928008 |
6.00E-05 |
35.36181 |
7037 |
0.117283 |
0.94995 |
|
4.24E-05 |
0.999958 |
20212.31 |
0.927782 |
5.00E-05 |
35.52261 |
7101 |
0.11835 |
0.94995 |
|
3.46E-05 |
0.999966 |
21664.93 |
0.929422 |
4.00E-05 |
37.82915 |
7528 |
0.125467 |
0.948949 |
|
2.61E-05 |
0.999974 |
23628.01 |
0.929533 |
3.00E-05 |
41.34171 |
8227 |
0.137117 |
0.948949 |
|
1.71E-05 |
0.999983 |
24909.99 |
0.929507 |
2.00E-05 |
43.52261 |
8661 |
0.14435 |
0.952953 |
|
8.61E-06 |
0.999991 |
28973.61 |
0.92908 |
1.00E-05 |
50.82412 |
10115 |
0.168583 |
0.948949 |
|
7.76E-06 |
0.999992 |
29809.58 |
0.929754 |
9.00E-06 |
52.65327 |
10478 |
0.174633 |
0.952953 |
|
6.95E-06 |
0.999993 |
30242.05 |
0.930247 |
8.00E-06 |
52.93467 |
10534 |
0.175567 |
0.953954 |
|
6.06E-06 |
0.999994 |
32418.75 |
0.931037 |
7.00E-06 |
56.50251 |
11245 |
0.187417 |
0.955956 |
|
5.12E-06 |
0.999995 |
33315.13 |
0.930222 |
6.00E-06 |
57.41709 |
11427 |
0.19045 |
0.950951 |
|
4.30E-06 |
0.999996 |
34408.14 |
0.93078 |
5.00E-06 |
59.24623 |
11806 |
0.196767 |
0.953954 |
|
3.49E-06 |
0.999997 |
36694.53 |
0.929985 |
4.00E-06 |
63.9397 |
12724 |
0.212067 |
0.950951 |
|
2.62E-06 |
0.999997 |
38486.48 |
0.931751 |
3.00E-06 |
67.08543 |
13366 |
0.222767 |
0.951952 |
|
1.71E-06 |
0.999998 |
41608.88 |
0.930795 |
2.00E-06 |
71.47739 |
14225 |
0.237083 |
0.948949 |
|
8.64E-07 |
0.999999 |
48418.2 |
0.931872 |
1.00E-06 |
83.41206 |
16599 |
0.27665 |
0.94995 |
|
训练集 |
0.5 |
p |
||||||
|
0.5 |
a |
|||||||
|
测试集 |
0.4 |
p |
||||||
|
0.6 |
a |
|||||||
|
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 |
最大准确率p-max |
|
0.515551 |
0.49964 |
174.9698 |
0.402669 |
0.5 |
2.256281 |
450 |
0.0075 |
0.565566 |
|
0.409463 |
0.592821 |
4006.583 |
0.919412 |
0.4 |
9.090452 |
1811 |
0.030183 |
0.971972 |
|
0.287786 |
0.716621 |
4784.146 |
0.928144 |
0.3 |
9.366834 |
1866 |
0.0311 |
0.963964 |
|
0.186042 |
0.819252 |
5421.779 |
0.929392 |
0.2 |
10.51759 |
2094 |
0.0349 |
0.956957 |
|
0.089566 |
0.911688 |
6021.839 |
0.926494 |
0.1 |
11.61809 |
2314 |
0.038567 |
0.951952 |
|
0.00859 |
0.991607 |
8307.709 |
0.923622 |
0.01 |
16.71859 |
3329 |
0.055483 |
0.951952 |
|
8.50E-04 |
0.99916 |
11852.64 |
0.919684 |
0.001 |
20.45226 |
4070 |
0.067833 |
0.942943 |
|
8.58E-05 |
0.999916 |
17950.76 |
0.921137 |
1.00E-04 |
30.26633 |
6023 |
0.100383 |
0.94995 |
|
7.64E-05 |
0.999925 |
18371.74 |
0.91997 |
9.00E-05 |
31.56281 |
6283 |
0.104717 |
0.944945 |
|
6.75E-05 |
0.999933 |
19029.53 |
0.919352 |
8.00E-05 |
32.43719 |
6455 |
0.107583 |
0.944945 |
|
5.99E-05 |
0.999941 |
19699.54 |
0.919236 |
7.00E-05 |
34.17085 |
6816 |
0.1136 |
0.944945 |
|
5.14E-05 |
0.999949 |
20016.31 |
0.920639 |
6.00E-05 |
35.60302 |
7089 |
0.11815 |
0.945946 |
|
4.26E-05 |
0.999958 |
20355.08 |
0.91994 |
5.00E-05 |
35.63317 |
7097 |
0.118283 |
0.942943 |
|
3.46E-05 |
0.999966 |
21334.22 |
0.920282 |
4.00E-05 |
37.80905 |
7527 |
0.12545 |
0.945946 |
|
2.58E-05 |
0.999974 |
23121.98 |
0.919427 |
3.00E-05 |
39.78392 |
7949 |
0.132483 |
0.940941 |
|
1.73E-05 |
0.999983 |
25287.12 |
0.919824 |
2.00E-05 |
42.67337 |
8499 |
0.14165 |
0.947948 |
|
8.63E-06 |
0.999991 |
29675.12 |
0.91999 |
1.00E-05 |
49.61809 |
9882 |
0.1647 |
0.941942 |
|
7.86E-06 |
0.999992 |
30146.41 |
0.920911 |
9.00E-06 |
50.54774 |
10066 |
0.167767 |
0.94995 |
|
6.90E-06 |
0.999993 |
31306.97 |
0.921067 |
8.00E-06 |
52.32663 |
10415 |
0.173583 |
0.94995 |
|
5.92E-06 |
0.999994 |
31242.93 |
0.919497 |
7.00E-06 |
52.58291 |
10467 |
0.17445 |
0.948949 |
|
5.16E-06 |
0.999995 |
32843.59 |
0.91913 |
6.00E-06 |
55.69849 |
11088 |
0.1848 |
0.948949 |
|
4.30E-06 |
0.999996 |
34360.62 |
0.92074 |
5.00E-06 |
57.33166 |
11411 |
0.190183 |
0.943944 |
|
3.45E-06 |
0.999997 |
36231.65 |
0.92071 |
4.00E-06 |
60.28141 |
12003 |
0.20005 |
0.947948 |
|
2.64E-06 |
0.999997 |
38178.81 |
0.922566 |
3.00E-06 |
63.62312 |
12667 |
0.211117 |
0.952953 |
|
1.73E-06 |
0.999998 |
42350.46 |
0.923617 |
2.00E-06 |
70.25628 |
13987 |
0.233117 |
0.946947 |
|
8.61E-07 |
0.999999 |
48665.55 |
0.922495 |
1.00E-06 |
81.49749 |
16226 |
0.270433 |
0.945946 |
|
训练集 |
0.5 |
p |
||||||
|
0.5 |
a |
|||||||
|
测试集 |
0.5 |
p |
||||||
|
0.5 |
a |
|||||||
|
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 |
最大准确率p-max |
|
0.515364 |
0.49962 |
175.2814 |
0.501768 |
0.5 |
2.20603 |
439 |
0.007317 |
0.551552 |
|
0.407196 |
0.597051 |
4086.05 |
0.932903 |
0.4 |
9.020101 |
1811 |
0.030183 |
0.974975 |
|
0.285219 |
0.719742 |
4760.03 |
0.936147 |
0.3 |
9.291457 |
1849 |
0.030817 |
0.95996 |
|
0.185463 |
0.818207 |
5363.698 |
0.93247 |
0.2 |
10.19095 |
2028 |
0.0338 |
0.954955 |
|
0.088918 |
0.911725 |
6068.291 |
0.928758 |
0.1 |
11.57286 |
2303 |
0.038383 |
0.955956 |
|
0.008788 |
0.991436 |
8312.714 |
0.92085 |
0.01 |
15.46734 |
3078 |
0.0513 |
0.947948 |
|
8.52E-04 |
0.999165 |
11708.52 |
0.914462 |
0.001 |
21.22613 |
4224 |
0.0704 |
0.944945 |
|
8.48E-05 |
0.999916 |
18032.69 |
0.909729 |
1.00E-04 |
30.88442 |
6178 |
0.102967 |
0.93994 |
|
7.58E-05 |
0.999925 |
18455.81 |
0.910896 |
9.00E-05 |
31.38191 |
6245 |
0.104083 |
0.93994 |
|
6.98E-05 |
0.999931 |
18907.17 |
0.911746 |
8.00E-05 |
32.30151 |
6428 |
0.107133 |
0.941942 |
|
6.00E-05 |
0.999941 |
19192.85 |
0.909608 |
7.00E-05 |
33.25628 |
6618 |
0.1103 |
0.93994 |
|
5.10E-05 |
0.99995 |
19637.51 |
0.910071 |
6.00E-05 |
33.23618 |
6614 |
0.110233 |
0.938939 |
|
4.33E-05 |
0.999957 |
21041.91 |
0.909015 |
5.00E-05 |
35.39196 |
7043 |
0.117383 |
0.948949 |
|
3.45E-05 |
0.999966 |
21458.21 |
0.910735 |
4.00E-05 |
36.67839 |
7299 |
0.12165 |
0.947948 |
|
2.56E-05 |
0.999975 |
23419.3 |
0.910383 |
3.00E-05 |
39.48241 |
7888 |
0.131467 |
0.936937 |
|
1.73E-05 |
0.999983 |
25467.01 |
0.911313 |
2.00E-05 |
43.12563 |
8582 |
0.143033 |
0.946947 |
|
8.62E-06 |
0.999991 |
28929.45 |
0.911504 |
1.00E-05 |
49.22613 |
9827 |
0.163783 |
0.947948 |
|
7.76E-06 |
0.999992 |
30596.92 |
0.909965 |
9.00E-06 |
51.05528 |
10192 |
0.169867 |
0.942943 |
|
7.03E-06 |
0.999993 |
31467.45 |
0.911711 |
8.00E-06 |
53.44724 |
10651 |
0.177517 |
0.943944 |
|
6.02E-06 |
0.999994 |
32308.31 |
0.911439 |
7.00E-06 |
53.8794 |
10722 |
0.1787 |
0.950951 |
|
5.21E-06 |
0.999995 |
33142.06 |
0.911681 |
6.00E-06 |
55.39196 |
11039 |
0.183983 |
0.946947 |
|
4.35E-06 |
0.999996 |
34622.05 |
0.911012 |
5.00E-06 |
58.53266 |
11648 |
0.194133 |
0.940941 |
|
3.42E-06 |
0.999997 |
37457.67 |
0.911821 |
4.00E-06 |
63.24121 |
12585 |
0.20975 |
0.937938 |
|
2.53E-06 |
0.999997 |
39145.41 |
0.910302 |
3.00E-06 |
65.72864 |
13080 |
0.218 |
0.942943 |
|
1.72E-06 |
0.999998 |
41515.22 |
0.912631 |
2.00E-06 |
69.12563 |
13787 |
0.229783 |
0.93994 |
|
8.68E-07 |
0.999999 |
48841.56 |
0.912908 |
1.00E-06 |
81.88945 |
16311 |
0.27185 |
0.946947 |
|
训练集 |
0.5 |
p |
||||||
|
0.5 |
a |
|||||||
|
测试集 |
0.6 |
p |
||||||
|
0.4 |
a |
|||||||
|
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 |
最大准确率p-max |
|
0.515048 |
0.499661 |
176.5377 |
0.599791 |
0.5 |
2.19598 |
437 |
0.007283 |
0.642643 |
|
0.405811 |
0.599618 |
4030.116 |
0.946625 |
0.4 |
8.728643 |
1769 |
0.029483 |
0.974975 |
|
0.287959 |
0.717861 |
4862.226 |
0.942691 |
0.3 |
-503.367 |
-100154 |
-1.66923 |
0.968969 |
|
0.187403 |
0.816228 |
5488.477 |
0.936565 |
0.2 |
10.29146 |
2048 |
0.034133 |
0.957958 |
|
0.089773 |
0.912661 |
6023.296 |
0.930322 |
0.1 |
11 |
2190 |
0.0365 |
0.957958 |
|
0.008599 |
0.991567 |
8373.266 |
0.91823 |
0.01 |
15.43719 |
3072 |
0.0512 |
0.953954 |
|
8.56E-04 |
0.999161 |
11769.97 |
0.906615 |
0.001 |
21.0804 |
4195 |
0.069917 |
0.940941 |
|
8.67E-05 |
0.999914 |
18349.92 |
0.904699 |
1.00E-04 |
450.9648 |
89742 |
1.4957 |
0.948949 |
|
7.72E-05 |
0.999924 |
18306.11 |
0.90159 |
9.00E-05 |
31.36683 |
6242 |
0.104033 |
0.93994 |
|
6.85E-05 |
0.999933 |
18780.89 |
0.900599 |
8.00E-05 |
32.02513 |
6373 |
0.106217 |
0.938939 |
|
5.99E-05 |
0.999941 |
19353.83 |
0.901826 |
7.00E-05 |
32.85427 |
6539 |
0.108983 |
0.936937 |
|
5.19E-05 |
0.999949 |
20324.79 |
0.900398 |
6.00E-05 |
34.42211 |
6850 |
0.114167 |
0.942943 |
|
4.29E-05 |
0.999958 |
20345.64 |
0.901369 |
5.00E-05 |
34.73367 |
6912 |
0.1152 |
0.951952 |
|
3.42E-05 |
0.999966 |
21949.56 |
0.901872 |
4.00E-05 |
37.62814 |
7504 |
0.125067 |
0.941942 |
|
2.60E-05 |
0.999974 |
22980.68 |
0.901671 |
3.00E-05 |
40.47236 |
8054 |
0.134233 |
0.941942 |
|
1.73E-05 |
0.999983 |
25387.27 |
0.899316 |
2.00E-05 |
43.0603 |
8585 |
0.143083 |
0.942943 |
|
8.60E-06 |
0.999991 |
30018.27 |
0.90076 |
1.00E-05 |
51.17085 |
10183 |
0.169717 |
0.948949 |
|
7.72E-06 |
0.999992 |
30042.71 |
0.900011 |
9.00E-06 |
51.28141 |
10220 |
0.170333 |
0.941942 |
|
6.86E-06 |
0.999993 |
31193.98 |
0.899377 |
8.00E-06 |
52.92462 |
10532 |
0.175533 |
0.935936 |
|
5.99E-06 |
0.999994 |
32049.32 |
0.902656 |
7.00E-06 |
54.29146 |
10819 |
0.180317 |
0.942943 |
|
5.12E-06 |
0.999995 |
33314.14 |
0.901328 |
6.00E-06 |
57.77387 |
11497 |
0.191617 |
0.940941 |
|
4.29E-06 |
0.999996 |
33135.32 |
0.901359 |
5.00E-06 |
56.50754 |
11260 |
0.187667 |
0.945946 |
|
3.40E-06 |
0.999997 |
34921.27 |
0.901826 |
4.00E-06 |
59.0402 |
11779 |
0.196317 |
0.944945 |
|
2.60E-06 |
0.999997 |
37738.74 |
0.901258 |
3.00E-06 |
63.25628 |
12588 |
0.2098 |
0.93994 |
|
1.72E-06 |
0.999998 |
42446.08 |
0.903401 |
2.00E-06 |
72.0603 |
14340 |
0.239 |
0.944945 |
|
8.57E-07 |
0.999999 |
46846.71 |
0.904553 |
1.00E-06 |
80.15075 |
15950 |
0.265833 |
0.941942 |
|
训练集 |
0.5 |
p |
||||||
|
0.5 |
a |
|||||||
|
测试集 |
0.7 |
p |
||||||
|
0.3 |
a |
|||||||
|
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 |
最大准确率p-max |
|
0.514892 |
0.499679 |
177.4121 |
0.700816 |
0.5 |
2.120603 |
422 |
0.007033 |
0.741742 |
|
0.416277 |
0.588445 |
4109.563 |
0.953763 |
0.4 |
9.19598 |
1830 |
0.0305 |
0.977978 |
|
0.289018 |
0.714385 |
4740.181 |
0.948823 |
0.3 |
9.281407 |
1847 |
0.030783 |
0.973974 |
|
0.185205 |
0.818787 |
5407.196 |
0.940554 |
0.2 |
10.13568 |
2017 |
0.033617 |
0.967968 |
|
0.089925 |
0.913099 |
6141.548 |
0.93325 |
0.1 |
11.48241 |
2285 |
0.038083 |
0.966967 |
|
0.008521 |
0.991705 |
8323.095 |
0.914553 |
0.01 |
17.06533 |
3412 |
0.056867 |
0.954955 |
|
8.59E-04 |
0.999152 |
11895.17 |
0.903748 |
0.001 |
20.65829 |
4111 |
0.068517 |
0.945946 |
|
8.55E-05 |
0.999916 |
18039.44 |
0.893582 |
1.00E-04 |
31.88945 |
6362 |
0.106033 |
0.958959 |
|
7.67E-05 |
0.999924 |
18449.29 |
0.890292 |
9.00E-05 |
31.45729 |
6275 |
0.104583 |
0.947948 |
|
6.87E-05 |
0.999932 |
18454.62 |
0.892928 |
8.00E-05 |
31.54774 |
6278 |
0.104633 |
0.938939 |
|
6.13E-05 |
0.99994 |
19463.43 |
0.891102 |
7.00E-05 |
33.33166 |
6648 |
0.1108 |
0.946947 |
|
5.13E-05 |
0.999949 |
20284.9 |
0.895443 |
6.00E-05 |
34.80905 |
6928 |
0.115467 |
0.953954 |
|
4.31E-05 |
0.999957 |
20506.18 |
0.88912 |
5.00E-05 |
35.17588 |
7016 |
0.116933 |
0.942943 |
|
3.49E-05 |
0.999965 |
21906.86 |
0.892546 |
4.00E-05 |
37.39698 |
7458 |
0.1243 |
0.93994 |
|
2.57E-05 |
0.999975 |
23162.81 |
0.889533 |
3.00E-05 |
39.74874 |
7925 |
0.132083 |
0.942943 |
|
1.71E-05 |
0.999983 |
25386.75 |
0.888703 |
2.00E-05 |
43.89447 |
8735 |
0.145583 |
0.938939 |
|
8.59E-06 |
0.999991 |
29183.94 |
0.890841 |
1.00E-05 |
50.56784 |
10063 |
0.167717 |
0.938939 |
|
7.68E-06 |
0.999992 |
29992.44 |
0.892118 |
9.00E-06 |
50.74874 |
10115 |
0.168583 |
0.943944 |
|
6.96E-06 |
0.999993 |
30287.49 |
0.892888 |
8.00E-06 |
51.13568 |
10191 |
0.16985 |
0.93994 |
|
5.89E-06 |
0.999994 |
31156.3 |
0.889694 |
7.00E-06 |
52.75377 |
10498 |
0.174967 |
0.944945 |
|
5.19E-06 |
0.999995 |
33318.47 |
0.892023 |
6.00E-06 |
56.57286 |
11273 |
0.187883 |
0.948949 |
|
4.35E-06 |
0.999996 |
35836.14 |
0.892878 |
5.00E-06 |
60.17085 |
11974 |
0.199567 |
0.941942 |
|
3.44E-06 |
0.999997 |
36361.22 |
0.892586 |
4.00E-06 |
62.66332 |
12486 |
0.2081 |
0.946947 |
|
2.53E-06 |
0.999997 |
38041.16 |
0.890665 |
3.00E-06 |
64.01005 |
12754 |
0.212567 |
0.93994 |
|
1.73E-06 |
0.999998 |
41794.77 |
0.895061 |
2.00E-06 |
71.06533 |
14144 |
0.235733 |
0.938939 |
|
8.62E-07 |
0.999999 |
48042.7 |
0.895856 |
1.00E-06 |
80.42211 |
16004 |
0.266733 |
0.942943 |
|
训练集 |
0.5 |
p |
||||||
|
0.5 |
a |
|||||||
|
测试集 |
0.8 |
p |
||||||
|
0.2 |
a |
|||||||
|
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 |
最大准确率p-max |
|
0.51531 |
0.499628 |
174.9196 |
0.799377 |
0.5 |
1.959799 |
406 |
0.006767 |
0.835836 |
|
0.407105 |
0.597558 |
4115.342 |
0.967928 |
0.4 |
9.311558 |
1855 |
0.030917 |
0.986987 |
|
0.287128 |
0.715635 |
4760.95 |
0.956036 |
0.3 |
9.175879 |
1826 |
0.030433 |
0.978979 |
|
0.18441 |
0.820183 |
5349.799 |
0.94664 |
0.2 |
10.1809 |
2026 |
0.033767 |
0.974975 |
|
0.089271 |
0.911917 |
5938.312 |
0.936097 |
0.1 |
11.12563 |
2214 |
0.0369 |
0.970971 |
|
0.008534 |
0.991737 |
8314.472 |
0.910252 |
0.01 |
15.31156 |
3047 |
0.050783 |
0.972973 |
|
8.60E-04 |
0.99916 |
11630.32 |
0.895654 |
0.001 |
20.23116 |
4026 |
0.0671 |
0.950951 |
|
8.59E-05 |
0.999915 |
17969.95 |
0.884543 |
1.00E-04 |
30.97487 |
6164 |
0.102733 |
0.951952 |
|
7.70E-05 |
0.999923 |
18655.23 |
0.888235 |
9.00E-05 |
32.68844 |
6505 |
0.108417 |
0.947948 |
|
6.80E-05 |
0.999933 |
18774.82 |
0.882541 |
8.00E-05 |
31.8794 |
6344 |
0.105733 |
0.945946 |
|
6.07E-05 |
0.99994 |
19908.6 |
0.887657 |
7.00E-05 |
34.21106 |
6841 |
0.114017 |
0.942943 |
|
5.11E-05 |
0.999949 |
19755.79 |
0.885649 |
6.00E-05 |
33.36683 |
6672 |
0.1112 |
0.943944 |
|
4.36E-05 |
0.999957 |
20452 |
0.881374 |
5.00E-05 |
34.92462 |
6965 |
0.116083 |
0.946947 |
|
3.46E-05 |
0.999966 |
22221.49 |
0.884291 |
4.00E-05 |
39.75377 |
7911 |
0.13185 |
0.947948 |
|
2.59E-05 |
0.999974 |
23015.72 |
0.88153 |
3.00E-05 |
39.38693 |
7838 |
0.130633 |
0.957958 |
|
1.73E-05 |
0.999983 |
25443.69 |
0.882999 |
2.00E-05 |
43.39196 |
8651 |
0.144183 |
0.94995 |
|
8.53E-06 |
0.999992 |
29146.75 |
0.885172 |
1.00E-05 |
50.31156 |
10012 |
0.166867 |
0.946947 |
|
7.67E-06 |
0.999992 |
30192.51 |
0.878351 |
9.00E-06 |
51.97487 |
10343 |
0.172383 |
0.941942 |
|
6.90E-06 |
0.999993 |
31619.56 |
0.881681 |
8.00E-06 |
53.47236 |
10641 |
0.17735 |
0.942943 |
|
6.09E-06 |
0.999994 |
31521.76 |
0.880046 |
7.00E-06 |
53.63819 |
10705 |
0.178417 |
0.942943 |
|
5.19E-06 |
0.999995 |
32457.08 |
0.882948 |
6.00E-06 |
55.30151 |
11005 |
0.183417 |
0.947948 |
|
4.34E-06 |
0.999996 |
34892.76 |
0.888557 |
5.00E-06 |
59.30151 |
11801 |
0.196683 |
0.942943 |
|
3.41E-06 |
0.999997 |
36031.88 |
0.881811 |
4.00E-06 |
61.28643 |
12196 |
0.203267 |
0.94995 |
|
2.59E-06 |
0.999997 |
38541.94 |
0.884422 |
3.00E-06 |
65.96482 |
13127 |
0.218783 |
0.941942 |
|
1.72E-06 |
0.999998 |
41538.48 |
0.880634 |
2.00E-06 |
70.19095 |
13983 |
0.23305 |
0.942943 |
|
8.66E-07 |
0.999999 |
50248.82 |
0.888783 |
1.00E-06 |
84.68342 |
16852 |
0.280867 |
0.955956 |
|
训练集 |
0.5 |
p |
||||||
|
0.5 |
a |
|||||||
|
测试集 |
0.9 |
p |
||||||
|
0.1 |
a |
|||||||
|
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 |
最大准确率p-max |
|
0.516515 |
0.49963 |
170.8995 |
0.900126 |
0.5 |
2.221106 |
442 |
0.007367 |
0.942943 |
|
0.410117 |
0.594254 |
4017.347 |
0.978853 |
0.4 |
9.477387 |
1901 |
0.031683 |
0.991992 |
|
0.287948 |
0.715286 |
4721.533 |
0.963396 |
0.3 |
9.221106 |
1835 |
0.030583 |
0.98999 |
|
0.186425 |
0.81875 |
5316.603 |
0.949195 |
0.2 |
10.06533 |
2003 |
0.033383 |
0.981982 |
|
0.089713 |
0.913228 |
6031.166 |
0.936132 |
0.1 |
11.01005 |
2191 |
0.036517 |
0.97998 |
|
0.008667 |
0.991602 |
8184.02 |
0.913813 |
0.01 |
15.10553 |
3006 |
0.0501 |
0.967968 |
|
8.47E-04 |
0.999165 |
11934.5 |
0.88993 |
0.001 |
20.75879 |
4131 |
0.06885 |
0.967968 |
|
8.44E-05 |
0.999916 |
18146.08 |
0.875262 |
1.00E-04 |
30.81407 |
6132 |
0.1022 |
0.966967 |
|
7.57E-05 |
0.999925 |
18287.8 |
0.873024 |
9.00E-05 |
31.19598 |
6208 |
0.103467 |
0.95996 |
|
6.81E-05 |
0.999932 |
18842.83 |
0.874754 |
8.00E-05 |
32.52764 |
6474 |
0.1079 |
0.970971 |
|
5.90E-05 |
0.999942 |
19146.66 |
0.874221 |
7.00E-05 |
33.21106 |
6609 |
0.11015 |
0.955956 |
|
5.21E-05 |
0.999949 |
19900.13 |
0.876298 |
6.00E-05 |
33.57286 |
6681 |
0.11135 |
0.94995 |
|
4.26E-05 |
0.999958 |
20773.77 |
0.869221 |
5.00E-05 |
35.47236 |
7074 |
0.1179 |
0.955956 |
|
3.45E-05 |
0.999966 |
21510.16 |
0.872777 |
4.00E-05 |
36.89447 |
7342 |
0.122367 |
0.943944 |
|
2.55E-05 |
0.999975 |
22580.6 |
0.868622 |
3.00E-05 |
38.20101 |
7602 |
0.1267 |
0.943944 |
|
1.70E-05 |
0.999983 |
25225.02 |
0.869679 |
2.00E-05 |
42.81407 |
8536 |
0.142267 |
0.955956 |
|
8.73E-06 |
0.999991 |
29139.24 |
0.873054 |
1.00E-05 |
49 |
9751 |
0.162517 |
0.950951 |
|
7.68E-06 |
0.999992 |
29208.69 |
0.869654 |
9.00E-06 |
49.03015 |
9757 |
0.162617 |
0.943944 |
|
6.90E-06 |
0.999993 |
30228.18 |
0.873034 |
8.00E-06 |
50.19095 |
9988 |
0.166467 |
0.938939 |
|
6.00E-06 |
0.999994 |
31077.35 |
0.876751 |
7.00E-06 |
52.10553 |
10385 |
0.173083 |
0.958959 |
|
5.23E-06 |
0.999995 |
32820.7 |
0.873089 |
6.00E-06 |
55.94472 |
11133 |
0.18555 |
0.946947 |
|
4.30E-06 |
0.999996 |
34875.83 |
0.87319 |
5.00E-06 |
58.28141 |
11598 |
0.1933 |
0.945946 |
|
3.41E-06 |
0.999997 |
36771.91 |
0.870805 |
4.00E-06 |
61.10553 |
12190 |
0.203167 |
0.947948 |
|
2.56E-06 |
0.999997 |
38024.93 |
0.871077 |
3.00E-06 |
63.25126 |
12587 |
0.209783 |
0.948949 |
|
1.73E-06 |
0.999998 |
42297.98 |
0.874804 |
2.00E-06 |
70.18593 |
13967 |
0.232783 |
0.961962 |
|
8.62E-07 |
0.999999 |
49040.7 |
0.875519 |
1.00E-06 |
82.47739 |
16413 |
0.27355 |
0.950951 |
|
训练集 |
0.5 |
p |
||||||
|
0.5 |
a |
|||||||
|
测试集 |
1 |
p |
||||||
|
0 |
a |
|||||||
|
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 |
最大准确率p-max |
|
0.514793 |
0.499623 |
178.8392 |
1 |
0.5 |
1.964824 |
391 |
0.006517 |
1 |
|
0.408172 |
0.596096 |
4051.658 |
0.990277 |
0.4 |
9.874372 |
1965 |
0.03275 |
1 |
|
0.285396 |
0.7193 |
4796.181 |
0.969366 |
0.3 |
10.18593 |
2043 |
0.03405 |
1 |
|
0.183454 |
0.819462 |
5451.658 |
0.956092 |
0.2 |
10.19095 |
2028 |
0.0338 |
0.994995 |
|
0.088923 |
0.912634 |
6081.799 |
0.938154 |
0.1 |
11.08543 |
2206 |
0.036767 |
0.995996 |
|
0.008548 |
0.991621 |
8227.578 |
0.905795 |
0.01 |
15.42714 |
3086 |
0.051433 |
0.97998 |
|
8.61E-04 |
0.999152 |
11658.61 |
0.884875 |
0.001 |
20.33668 |
4077 |
0.06795 |
0.963964 |
|
8.50E-05 |
0.999916 |
18020.46 |
0.862712 |
1.00E-04 |
31.12563 |
6194 |
0.103233 |
0.964965 |
|
7.70E-05 |
0.999924 |
18260.02 |
0.871288 |
9.00E-05 |
31.11558 |
6207 |
0.10345 |
0.971972 |
|
6.93E-05 |
0.999932 |
19365.37 |
0.863804 |
8.00E-05 |
32.24623 |
6448 |
0.107467 |
0.978979 |
|
6.03E-05 |
0.99994 |
19051.94 |
0.868844 |
7.00E-05 |
32.05025 |
6378 |
0.1063 |
0.948949 |
|
5.22E-05 |
0.999948 |
20186.96 |
0.866203 |
6.00E-05 |
34.21106 |
6808 |
0.113467 |
0.956957 |
|
4.33E-05 |
0.999957 |
21040.99 |
0.863632 |
5.00E-05 |
35.67839 |
7100 |
0.118333 |
0.955956 |
|
3.46E-05 |
0.999966 |
22071.54 |
0.866319 |
4.00E-05 |
36.88945 |
7357 |
0.122617 |
0.966967 |
|
2.59E-05 |
0.999974 |
23027.16 |
0.861138 |
3.00E-05 |
38.36181 |
7634 |
0.127233 |
0.941942 |
|
1.72E-05 |
0.999983 |
25235.65 |
0.867254 |
2.00E-05 |
43.06533 |
8570 |
0.142833 |
0.945946 |
|
8.70E-06 |
0.999991 |
29858.89 |
0.864281 |
1.00E-05 |
50.26633 |
10035 |
0.16725 |
0.942943 |
|
7.57E-06 |
0.999992 |
29416.11 |
0.863567 |
9.00E-06 |
48.88945 |
9729 |
0.16215 |
0.952953 |
|
6.94E-06 |
0.999993 |
30564.27 |
0.865152 |
8.00E-06 |
51.44221 |
10237 |
0.170617 |
0.950951 |
|
6.03E-06 |
0.999994 |
31670.24 |
0.866404 |
7.00E-06 |
52.32663 |
10428 |
0.1738 |
0.951952 |
|
5.17E-06 |
0.999995 |
32734.45 |
0.859528 |
6.00E-06 |
54.27136 |
10800 |
0.18 |
0.952953 |
|
4.23E-06 |
0.999996 |
33449.26 |
0.862541 |
5.00E-06 |
55.37186 |
11034 |
0.1839 |
0.943944 |
|
3.43E-06 |
0.999997 |
35864.78 |
0.863758 |
4.00E-06 |
60.89447 |
12133 |
0.202217 |
0.950951 |
|
2.57E-06 |
0.999997 |
38843.03 |
0.867184 |
3.00E-06 |
64.20101 |
12776 |
0.212933 |
0.945946 |
|
1.69E-06 |
0.999998 |
41138.88 |
0.862033 |
2.00E-06 |
68.14573 |
13561 |
0.226017 |
0.940941 |
|
8.67E-07 |
0.999999 |
49087.97 |
0.86898 |
1.00E-06 |
81.9196 |
16302 |
0.2717 |
0.95996 |
二分类排斥子和鞍点的准确率的表达式pa相关推荐
- 二分类吸引子和鞍点的准确率的表达式ca
r1 r2 <1 <1 吸引子 c >1 >1 排斥子 p >1 <1 鞍点 a <1 >1 反鞍点 fa 制作一个二分类网络用来分类c和a,通 ...
- 二分类2x2对角矩阵准确率表达式
前面的实验中分别测量了吸引子和鞍点,排斥子和鞍点,吸引子和排斥子的分类网络的准确率. 以吸引子和鞍点为例 r1 r2 <1 <1 吸引子 c >1 >1 排斥子 p & ...
- 二分类吸引子，排斥子，鞍点和反鞍点数据汇总
r1 r2 <1 <1 吸引子 c >1 >1 排斥子 p >1 <1 鞍点 a <1 >1 反鞍点 fa 图中的2*2数组有可能构成4种对象, ...
- 是否所有二分类神经网络的准确率都能无限趋近100%？
制作一个二分类的神经网络验证是否只要经过足够的迭代都能使准确率无限上升并接近1. r1 r2 <1 <1 吸引子 c >1 >1 排斥子 p >1 <1 鞍 ...
- 吸引子矩阵和鞍点矩阵可以用神经网络二分类吗？
制作一个4*4*2结构的神经网络,向这个网络输入吸引子并让这个网络向1,0收敛 将这个网络简单表示成 s(c)-4-4-2-(2*k),k∈(0,1) 用同样的办法制作一个4*4*2的网络向这个网络输 ...
- 二分类minst0-1到0-9近似迭代次数公式和准确率公式汇总
在前面的陆续实验中已经将二分类minst0,1到二分类minst0,9这9个实验都做完了,并得到了各自网络的迭代次数与准确率公式,可以近似的估算预期准确率的网络训练时间.实验的具体过程以minst0, ...
- 准确率(Accuracy), 精确率(Precision), 召回率(Recall)和F1-Measure（对于二分类问题）
首先我们可以计算准确率(accuracy),其定义是: 对于给定的测试数据集,分类器正确分类的样本数与总样本数之比.也就是损失函数是0-1损失时测试数据集上的准确率. 下面在介绍时使用一下例子: 一个 ...
- 在二分类问题中，准确率一直处于50%上下的解决方法
最近在 猫狗识别 项目中,不知为何准确率(训练集准确率和验证集准确率)一直处于0.5左右,这说明网络根本没有学习.后来查阅了许多他人的经验,并做了总结. 首先谈谈我的是如何解决的: 网络结构:Alex ...
- 二分类神经网络结果准确率50%，loss一直在0.69的解决方法
先参考这篇文章,了解为什么loss是0.69 链接:https://www.jianshu.com/p/45c2180cab17 这个问题很多人在训练自己或者迁移别的网络的时候都会遇到,特别是二分类这 ...
最新文章
- 解决push的时候有时候会卡一下的问题
- python调用百度地图画轨迹图_[python]百度地图API，正/逆地理编码，路线规划接口的调用，实现输出出行的距离和......
- 时代风口下,东软集团软件业务怎么转型?
- android studio 创建.9文件,自己使用Android studio创建.9(点9)图片
- php如何按日期统计,关于按日获取统计信息：按日期获取统计信息 – 日期时间列 – mysql / php...
- 完整部署CentOS7.2+OpenStack+kvm 云平台环境（3）--为虚拟机指定固定ip
- php 502状态码,Nginx502状态码处理
- jquey 阻止表单提交
- 完全公平调度 c语言,使用完全公平调度程序（CFS）进行多任务处理
- 大数据面试3分钟自我介绍_面试的时候，如何自我介绍？
- 【Matlab图像隐写】DCT数字水印嵌入与提取【含GUI源码 943期】
- 微信互动营销有哪些方式？
- 第九天 杭电OJ 1087、1203、1003
- python下载图片插入excel_Python向Excel中插入图片的简单实现方法
- 图解IFRS 9 金融工具（13）实施时间及结语
- 【解决方案】摄像机户外直播能在哪些地方运用？团建/项目启动会/户外婚礼等户外直播方案介绍
- 计算机之父阿兰·图灵传奇的一生
- 11月微博博客日均覆盖数TOP10：网易博客升至第七
- firstElementChild与firstChild区别
- [转]看看人家外国人的带薪假怎么休。