本文构造了一个带有1个卷积核的网络二分类minst的0和2，通过调整学习率r观察学习率对神经网络的迭代次数和准确率到底有什么影响。
实验用minst数据集将28*28的图片缩小到9*9，网络用一个3*3的卷积核，网络结构是81*49*30*2，画成图
这个网络由两部分组成，左右两边分别向1,0和0,1收敛，左边输入minst的0，右边输入minst的2，让左右两个网络的权重部分共享，实现耦合达到分类的目的。
| 具体进样顺序 |
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| 学习率用r表示 |
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| r=0.6 |
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| δ=0.1 |
迭代次数 |
||
| minst 0-1 |
1
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|
判断是否达到收敛 |
|
| minst 2-1 |
2 |
判断是否达到收敛 |
|
| 梯度下降 |
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| minst 0-2 |
3 |
判断是否达到收敛 |
|
| minst 2-2 |
4 |
判断是否达到收敛 |
|
| 梯度下降 |
|||
| minst 0-3 |
5 |
判断是否达到收敛 |
|
| minst 2-3 |
6 |
判断是否达到收敛 |
|
| 梯度下降 |
|||
| minst 0-4 |
7 |
判断是否达到收敛 |
|
| minst 2-4 |
8 |
判断是否达到收敛 |
|
| 梯度下降 |
|||
| minst 0-5 |
9 |
判断是否达到收敛 |
|
| minst 2-5 |
10 |
判断是否达到收敛 |
|
| 梯度下降 |
|||
| …… |
|||
| minst 0-4999 |
9997 |
判断是否达到收敛 |
|
| minst 2-4999 |
9998 |
判断是否达到收敛 |
|
| 梯度下降 |
|||
| …… |
|||
| 如果4999图片内没有达到收敛标准再次从头循环 |
|||
| minst 0-1 |
9999 |
判断是否达到收敛 |
|
| minst 2-1 |
10000 |
判断是否达到收敛 |
|
| …… |
|||
| 每当网路达到收敛标准记录迭代次数和对应的准确率测试结果 |
|||
| 将这一过程重复相应迭代批次 |
|||
| δ=0.01 |
|||
| … |
|||
| δ=1e-7 |
|||
| r=0.5 |
|||
| … |
|||
| r=1e-3 |
|||
收敛条件是
if (Math.abs(f2[0]-y[0])< δ && Math.abs(f2[1]-y[1])< δ )
具体的迭代批次数据
| 0.6 |
0.5 |
0.4 |
0.3 |
0.2 |
0.1 |
9*1e-2 |
8*1e-2 |
7*1e-2 |
6*1e-2 |
5*1e-2 |
4*1e-2 |
3*1e-2 |
2*1e-2 |
1.00E-02 |
1.00E-03 |
|
| 迭代批次 |
99 |
99 |
99 |
99 |
99 |
199 |
199 |
199 |
99 |
99 |
99 |
99 |
49 |
19 |
19 |
9 |
| 收敛标准数量 |
23 |
22 |
22 |
22 |
22 |
23 |
23 |
23 |
23 |
23 |
23 |
23 |
23 |
22 |
22 |
19 |
| 收敛次数 |
2277 |
2178 |
2178 |
2178 |
2178 |
4577 |
4577 |
4577 |
2277 |
2277 |
2277 |
2277 |
1127 |
418 |
418 |
171 |
比如当r=1e-3时尝试了19个不同的收敛标准，每个收敛标准重复9次取平均值。
首先拟合迭代次数曲线n（r，δ）
具体数据
| δ\r |
0.6 |
0.5 |
0.4 |
0.3 |
0.2 |
0.1 |
9*1e-2 |
8*1e-2 |
7*1e-2 |
6*1e-2 |
5*1e-2 |
4*1e-2 |
3*1e-2 |
2*1e-2 |
1.00E-02 |
1.00E-03 |
| 0.1 |
450.939394 |
509.707071 |
601.646465 |
832.161616 |
1346.90909 |
2785.725 |
3022.15075 |
3421.48744 |
3760.23232 |
4466.89899 |
5034.78788 |
6573.75758 |
8421.97959 |
13866.1053 |
29395.1053 |
266650.667 |
| 1.00E-02 |
707.212121 |
757.222222 |
998.060606 |
1109.79798 |
1717.31313 |
3620.905 |
4132.17085 |
4517.59296 |
5232.73737 |
6364.59596 |
7586.88889 |
9070.87879 |
13176.8163 |
17543.2105 |
33094.2105 |
403032.778 |
| 1.00E-03 |
1102.33333 |
1333.44444 |
1450.57576 |
1674.44444 |
2429.12121 |
4846.435 |
5419.32161 |
6337.56784 |
7445.60606 |
8704.9697 |
10398.8384 |
13593.1111 |
18510.2857 |
26145.6316 |
59695.1053 |
540944.111 |
| 1.00E-04 |
2526.92929 |
2765.44444 |
3067.30303 |
3262.0101 |
3964.62626 |
7709.22 |
8515.03015 |
9764.71357 |
11357.2323 |
13413.596 |
17218.1616 |
21796.3434 |
28744.2041 |
42848.8421 |
92427 |
886770.222 |
| 0.00009 |
2685.73737 |
2779.69697 |
3145.35354 |
3561.0202 |
4488.29293 |
7951.16 |
9066.59296 |
9938.67337 |
11038.9899 |
14057.1313 |
17463.9192 |
22672.101 |
31534.4694 |
46692.3684 |
102512.526 |
900392.778 |
| 0.00008 |
2923.36364 |
3232.07071 |
3656.38384 |
3935.35354 |
4710.14141 |
8182.51 |
8945.27136 |
10424.4472 |
12044.9899 |
14629.4242 |
18146.7879 |
22694.5758 |
31008.0816 |
50762.3684 |
96688.8421 |
1095102.56 |
| 0.00007 |
2631.68687 |
3063.91919 |
3951.42424 |
4071.9596 |
4461.30303 |
8193.28 |
9274.56784 |
10642.4271 |
12481.7172 |
14741.697 |
18819.2626 |
24147.9394 |
34670.9388 |
46805.1579 |
107360.526 |
1165418.56 |
| 0.00006 |
3412.84848 |
3509.91919 |
4434.29293 |
4480.06061 |
5156.35354 |
8780.59 |
9656.64322 |
10810.8693 |
12898.6061 |
15812.2424 |
19477.4141 |
24635.6667 |
34407.5306 |
53700.5789 |
101850.789 |
1237129.56 |
| 0.00005 |
3513.75758 |
3871.20202 |
4466.68687 |
4494.32323 |
5562.42424 |
9227.095 |
9912.12563 |
11487.005 |
13513.3131 |
16702.0101 |
19825.8586 |
26156.1616 |
34971.2449 |
54688.1579 |
114240.842 |
1157092.44 |
| 0.00004 |
4030.0303 |
4433.72727 |
5692.38384 |
5888.41414 |
6443.23232 |
9473.415 |
10787.3367 |
12214.598 |
15033.7879 |
17546.2121 |
21844.7677 |
27275.6263 |
37760.8776 |
56086.2105 |
110557.263 |
1369831.33 |
| 0.00003 |
5155.45455 |
6595.0101 |
6183.33333 |
6651.49495 |
6918.32323 |
10478 |
11589.6834 |
13443.5025 |
16093.1515 |
19075.0202 |
22692.5253 |
31011.0808 |
41042.6939 |
66337.2105 |
145883.526 |
1074014.33 |
| 0.00002 |
6345.70707 |
6690.9798 |
8425.63636 |
8510.67677 |
12240.6061 |
12060.835 |
13977.1156 |
16357.5477 |
18447.9091 |
20868.303 |
25713.101 |
34325.6061 |
46288.6531 |
73116.6316 |
157999.105 |
1733106.33 |
| 1.00E-05 |
10469.1717 |
12805.1616 |
14396.6364 |
16427.8485 |
17083.1818 |
19757.36 |
19716.1307 |
21967.5829 |
28235.1414 |
26126.2929 |
31903.6364 |
42933.4343 |
57378.7755 |
88206.8421 |
184000.684 |
1880675.11 |
| 0.000009 |
14148.7374 |
15900.1717 |
14248.5556 |
20380.202 |
18224.7172 |
22245.54 |
22123.0352 |
23800.6633 |
24885.101 |
30206.6364 |
36951.2222 |
45709.4141 |
61453.449 |
99080.4211 |
197259.947 |
1782400 |
| 0.000008 |
12061.3333 |
16990.6263 |
17342.9091 |
20878.4949 |
18792.8788 |
22214.925 |
24338.4271 |
23525.5578 |
26229.3838 |
30248.3333 |
37264.596 |
45743.9798 |
63118.7959 |
102146.895 |
238433.632 |
2161505.67 |
| 0.000007 |
15447.697 |
17055.3131 |
16756.8081 |
23393.7879 |
24626.9596 |
28045.605 |
26621.3769 |
27583.9397 |
27712.8889 |
33021.6768 |
40300.8586 |
48154.7172 |
66872.6327 |
91680.1579 |
227748.684 |
2427938.56 |
| 0.000006 |
16363.4242 |
19538.9798 |
23098.0606 |
23525.2424 |
25262.9596 |
28410.335 |
30478.005 |
30443.5075 |
34617.6162 |
36609.3333 |
43348.8081 |
51335.0707 |
72964.4286 |
101187.316 |
235256.158 |
2245382.33 |
| 0.000005 |
15435.3636 |
22017.1515 |
28748.1313 |
27564.8283 |
27952.2626 |
33681.76 |
32414.6985 |
34133.2814 |
39291.5354 |
39831.3737 |
49829.5758 |
57589.2424 |
79256.6327 |
112888.316 |
231381.368 |
2399699.89 |
| 0.000004 |
22163.8384 |
27614.2727 |
32165.7879 |
35140.7172 |
38190.202 |
37281.575 |
40560.4121 |
38845.3166 |
49073 |
49934.8485 |
56060.8586 |
59055.5657 |
80945.449 |
105719.421 |
235798.316 |
2647137.78 |
| 0.000003 |
22927.2121 |
28859.899 |
36659.5859 |
35043.4141 |
42120.1818 |
45173.585 |
51076.7688 |
54023.8342 |
56240.5253 |
55068.7475 |
61498.8384 |
72600.9091 |
94621.9388 |
130338.421 |
311185.947 |
|
| 0.000002 |
33399.2828 |
36129.6364 |
41347.3737 |
47989.303 |
54458.3232 |
60366.615 |
70687.1256 |
73467.4171 |
65498.101 |
79889.0505 |
84106.9394 |
99886.303 |
110986.531 |
195190 |
346350.474 |
|
| 1.00E-06 |
44776.8283 |
49352.596 |
58196.596 |
66137.7778 |
74129.0202 |
90392.445 |
100523.663 |
110144.452 |
103614.222 |
111489.737 |
127626.586 |
135720.404 |
159008.306 |
232212.316 |
477003.316 |
|
| 0.0000001 |
91114.8283 |
207402.66 |
212024.005 |
243067.714 |
255093.152 |
300479.242 |
360578.293 |
405314.465 |
481567.265 |
同时考虑r和δ两个变量得到是一个二维曲面，一个最初的印象是
| δ |
r |
n |
| 不变 |
减小 |
增大 |
| 减小 |
不变 |
增大 |
画成图
r=0.1不变
对应不同r的曲线
δ=1e-5不变
因为在r不变的情况下可以用
很好的拟合n和δ的数据，所以先将对应每个r的n（δ）曲线拟合出来
比如第一组r=0.6的数据
| x |
δ |
1.00E-06,0.000002,0.000003,0.000004,0.000005,0.000006,0.000007,0.000008,0.000009,1.00E-05,0.00002,0.00003,0.00004,0.00005,0.00006,0.00007,0.00008,0.00009,1.00E-04 |
| y |
n |
44776.82828,33399.28283,22927.21212,22163.83838,15435.36364,16363.42424,15447.69697,12061.33333,14148.73737,10469.17172,6345.707071,5155.454545,4030.030303,3513.757576,3412.848485,2631.686869,2923.363636,2685.737374,2526.929293 |
| 拟合方程式：y=ax^b |
||
| a = 5.35983868500963 |
||
| b = -0.661586726096118 |
||
| 相关系数 R2：0.979033223519759 |
得到当r=0.6时n（δ）曲线的表达式是
用同样的办法将所有的r的n（δ）曲线都拟合出来，得到的数据表格
| r |
a |
b |
| 0.6 |
5.35983869 |
-0.6615867 |
| 0.5 |
4.67754841 |
-0.68712 |
| 0.4 |
5.52390567 |
-0.6841599 |
| 0.3 |
5.0193023 |
-0.7009318 |
| 0.2 |
7.53120014 |
-0.6739942 |
| 0.1 |
44.4044341 |
-0.5407144 |
| 0.09 |
52.9869736 |
-0.5320451 |
| 0.08 |
79.3951193 |
-0.5032233 |
| 0.07 |
136.397228 |
-0.464944 |
| 0.06 |
230.86659 |
-0.4290442 |
| 0.05 |
369.07106 |
-0.4035616 |
| 0.04 |
679.834517 |
-0.3674333 |
| 0.03 |
1196.15737 |
-0.3429743 |
| 0.02 |
1962.58277 |
-0.3350236 |
| 1.00E-02 |
3954.43475 |
-0.3399457 |
| 1.00E-03 |
53754.1569 |
-0.311865 |
然后再用r去拟合a和b
| x |
r |
1.00E-03,1.00E-02,0.02,0.03,0.04,0.05,0.06 |
| y |
a |
53754.15692,3954.434749,1962.582766,1196.157366,679.8345172,369.0710596,230.8665897 |
| 拟合方程式：y=cx^d |
||
| c = 10.2471727161249 |
||
| d = -1.26716462098638 |
||
| 相关系数 R2：0.947301598419259 |
||
| x |
r |
1.00E-03,1.00E-02,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09,0.1,0.2 |
| y |
b |
0.311864963,0.339945742,0.335023592,0.342974299,0.367433328,0.403561618,0.42904418,0.464943989,0.503223333,0.532045068,0.54071441,0.673994167 |
| 拟合方程式：Y = m + n·X |
||
| m = 0.312611765764848 |
||
| n = 1.98858521946981 |
||
| 相关系数 R2：0.947569463434112 |
所以得到n（r，δ）的近似表达式
带入数据
与实测数据比较
| 计算值 |
0.6 |
0.5 |
0.4 |
0.3 |
0.2 |
0.1 |
0.09 |
0.08 |
0.07 |
0.06 |
0.05 |
0.04 |
0.03 |
0.02 |
1.00E-02 |
1.00E-03 |
| 0.0000001 |
6.793E+11 |
3.47E+10 |
1.867E+09 |
109015644 |
7389512.4 |
721208.26 |
598193.93 |
504033.85 |
433255.49 |
382274.84 |
349550.9 |
336597.12 |
351744.55 |
426739.68 |
745446.75 |
10334760 |
| 1.00E-06 |
2.12E+10 |
1.712E+09 |
145591050 |
13436975 |
1439749.6 |
222121.4 |
192866.84 |
170122.25 |
153084.54 |
141399.81 |
135353.47 |
136444.25 |
149265.03 |
189574.34 |
346672.19 |
5008411.7 |
| 0.000002 |
7.465E+09 |
691938429 |
67542890 |
7154986.8 |
879947.37 |
155819.75 |
137175.3 |
122677.72 |
111923.73 |
104815.59 |
101726.17 |
103969.21 |
115317.13 |
148491.49 |
275313.34 |
4027132.2 |
| 0.000003 |
4.054E+09 |
407317448 |
43098529 |
4948902.4 |
659741.8 |
126635.96 |
112385.99 |
101321.99 |
93188.408 |
87976.634 |
86074.769 |
88684.895 |
99160.898 |
128721.15 |
240589.86 |
3544847.1 |
| 0.000004 |
2.629E+09 |
279672673 |
31334632 |
3809922.8 |
537806.96 |
109308.67 |
97565.044 |
88464.754 |
81830.081 |
77696.766 |
76453.255 |
79223.547 |
89090.13 |
116311.74 |
218642.96 |
3238111.1 |
| 0.000005 |
1.879E+09 |
208928609 |
24470557 |
3110332.5 |
458974.38 |
97518.725 |
87428.855 |
79626.56 |
73982.295 |
70557.78 |
69737.292 |
72585.608 |
81988.505 |
107516.24 |
203008 |
3018587.8 |
| 0.000006 |
1.428E+09 |
164632509 |
19994356 |
2635216.1 |
403221.54 |
88836.034 |
79933.811 |
73064.813 |
68132.246 |
65214.534 |
64690.299 |
67577.043 |
76608.37 |
100825.86 |
191066.95 |
2850318.3 |
| 0.000007 |
1.132E+09 |
134593022 |
16854943 |
2290599 |
361401.12 |
82100.89 |
74100.395 |
67940.63 |
63548.499 |
61013.829 |
60709.175 |
63612.967 |
72335.916 |
95495.076 |
181520.6 |
2715387.9 |
| 0.000008 |
925680110 |
113040121 |
14536825 |
2028726.6 |
328697.3 |
76680.813 |
69392.508 |
63793.268 |
59827.905 |
57594.365 |
57459.16 |
60367.587 |
68828.033 |
91105.705 |
173637.58 |
2603679.9 |
| 0.000009 |
775242122 |
96912804 |
12758224 |
1822704.6 |
302315.93 |
72197.758 |
65488.765 |
60345.693 |
56727.342 |
54737.612 |
54737.169 |
57642.67 |
65875.336 |
87401.779 |
166968.92 |
2508967.3 |
| 1.00E-05 |
661511548 |
84446274 |
11352430 |
1656205.3 |
280516.34 |
68410.083 |
62183.209 |
57419.913 |
54090.204 |
52302.441 |
52411.715 |
55309.54 |
63341.557 |
84216.287 |
161220.92 |
2427167.1 |
| 0.00002 |
232947274 |
34132105 |
5266641.9 |
881904.39 |
171446.21 |
47990.162 |
44227.406 |
41406.365 |
39546.626 |
38770.287 |
39390.515 |
42145.342 |
48935.553 |
65965.688 |
128035.27 |
1951621.2 |
| 0.00003 |
126504492 |
20092253 |
3360598.2 |
609988.38 |
128542.04 |
39001.99 |
36234.957 |
34198.347 |
32926.772 |
32541.717 |
33329.964 |
35949.635 |
42079.554 |
57182.935 |
111887.02 |
1717897.2 |
| 0.00004 |
82030967 |
13795761 |
2443311 |
469600.82 |
104784.64 |
33665.442 |
31456.457 |
29858.755 |
28913.472 |
28739.292 |
29604.311 |
32114.348 |
37805.96 |
51670.195 |
101680.55 |
1569247.3 |
| 0.00005 |
58621150 |
10306081 |
1908086.3 |
383371.2 |
89425.141 |
30034.315 |
28188.395 |
26875.675 |
26140.571 |
26098.649 |
27003.748 |
29423.569 |
34792.341 |
47762.892 |
94409.463 |
1462862.4 |
| 0.00006 |
44547788 |
8121032 |
1559055.4 |
324809.63 |
78562.431 |
27360.175 |
25771.879 |
24660.944 |
24073.541 |
24122.234 |
25049.446 |
27393.278 |
32509.246 |
44790.762 |
88856.244 |
1381315.9 |
| 0.00007 |
35319956 |
6639236.9 |
1314260.5 |
282333.06 |
70414.27 |
25285.851 |
23891.097 |
22931.422 |
22453.94 |
22568.433 |
23507.871 |
25786.386 |
30696.203 |
42422.622 |
84416.687 |
1315926.2 |
| 0.00008 |
28886707 |
5576070.3 |
1133505.7 |
250055.37 |
64042.36 |
23616.548 |
22373.202 |
21531.598 |
21139.322 |
21303.606 |
22249.397 |
24470.827 |
29207.611 |
40472.693 |
80750.667 |
1261790.5 |
| 0.00009 |
24192150 |
4780538.1 |
994819.68 |
224661.66 |
58902.295 |
22235.834 |
21114.576 |
20367.968 |
20043.783 |
20246.92 |
21195.385 |
23366.244 |
27954.616 |
38827.265 |
77649.386 |
1215891 |
| 1.00E-04 |
20643082 |
4165586.1 |
885203.19 |
204139.4 |
54654.932 |
21069.286 |
20048.814 |
19380.454 |
19111.989 |
19346.174 |
20294.92 |
22420.478 |
26879.39 |
37412.146 |
74976.26 |
1176249.2 |
| 1.00E-03 |
644186.52 |
205481.03 |
69023.522 |
25161.672 |
10648.797 |
6489.0264 |
6464.0433 |
6541.3196 |
6752.9441 |
7155.9649 |
7858.6205 |
9088.4475 |
11406.439 |
16619.928 |
34867.929 |
570031.65 |
| 1.00E-02 |
20102.438 |
10136.018 |
5382.0938 |
3101.3599 |
2074.7785 |
1998.5235 |
2084.1061 |
2207.8359 |
2386.0549 |
2646.9231 |
3043.0234 |
3684.1264 |
4840.3946 |
7383.2178 |
16215.432 |
276247.67 |
| 0.1 |
627.31524 |
499.99193 |
419.66757 |
382.26526 |
404.24339 |
615.51546 |
671.94757 |
745.19203 |
843.07787 |
979.07158 |
1178.3228 |
1493.411 |
2054.0521 |
3279.9121 |
7541.0343 |
133874.63 |
|
实测值/计算值 |
0.6 |
0.5 |
0.4 |
0.3 |
0.2 |
0.1 |
0.09 |
0.08 |
0.07 |
0.06 |
0.05 |
0.04 |
0.03 |
0.02 |
1.00E-02 |
1.00E-03 |
| 0.0000001 |
1.34E-07 |
0 |
0 |
0 |
0 |
0.2875767 |
0.3544403 |
0.4822448 |
0.5887823 |
0.7860293 |
1.0315473 |
1.2041531 |
1.3690824 |
0 |
0 |
0 |
| 1.00E-06 |
2.112E-06 |
2.883E-05 |
0.0003997 |
0.0049221 |
0.0514874 |
0.4069506 |
0.5212076 |
0.647443 |
0.6768431 |
0.7884716 |
0.9429133 |
0.994695 |
1.065275 |
1.2249143 |
1.3759492 |
0 |
| 0.000002 |
4.474E-06 |
5.222E-05 |
0.0006122 |
0.0067071 |
0.0618882 |
0.3874131 |
0.5153051 |
0.5988652 |
0.585203 |
0.7621867 |
0.8267975 |
0.9607296 |
0.9624462 |
1.3144861 |
1.2580229 |
0 |
| 0.000003 |
5.656E-06 |
7.085E-05 |
0.0008506 |
0.007081 |
0.0638434 |
0.35672 |
0.4544763 |
0.5331896 |
0.6035142 |
0.6259474 |
0.7144816 |
0.8186389 |
0.9542263 |
1.0125641 |
1.2934292 |
0 |
| 0.000004 |
8.431E-06 |
9.874E-05 |
0.0010265 |
0.0092235 |
0.071011 |
0.341067 |
0.4157269 |
0.439105 |
0.5996939 |
0.6426889 |
0.7332697 |
0.7454295 |
0.9085793 |
0.9089316 |
1.0784629 |
0.8174944 |
| 0.000005 |
8.217E-06 |
0.0001054 |
0.0011748 |
0.0088623 |
0.0609016 |
0.3453876 |
0.3707552 |
0.428667 |
0.5310938 |
0.5645214 |
0.7145327 |
0.7933975 |
0.9666798 |
1.0499652 |
1.1397648 |
0.7949743 |
| 0.000006 |
1.146E-05 |
0.0001187 |
0.0011552 |
0.0089273 |
0.0626528 |
0.3198064 |
0.3812905 |
0.4166644 |
0.5080945 |
0.5613677 |
0.6700975 |
0.7596525 |
0.9524342 |
1.003585 |
1.231276 |
0.7877655 |
| 0.000007 |
1.365E-05 |
0.0001267 |
0.0009942 |
0.010213 |
0.068143 |
0.3415993 |
0.3592609 |
0.4060006 |
0.4360904 |
0.5412163 |
0.6638347 |
0.7569953 |
0.9244734 |
0.9600512 |
1.2546713 |
0.8941406 |
| 0.000008 |
1.303E-05 |
0.0001503 |
0.001193 |
0.0102914 |
0.0571738 |
0.2897064 |
0.3507357 |
0.3687781 |
0.4384139 |
0.5251961 |
0.6485406 |
0.7577573 |
0.9170507 |
1.121191 |
1.3731683 |
0.8301733 |
| 0.000009 |
1.825E-05 |
0.0001641 |
0.0011168 |
0.0111813 |
0.0602837 |
0.3081195 |
0.3378142 |
0.3944053 |
0.4386791 |
0.5518442 |
0.6750664 |
0.7929788 |
0.9328749 |
1.1336202 |
1.1814171 |
0.7104118 |
| 1.00E-05 |
1.583E-05 |
0.0001516 |
0.0012682 |
0.009919 |
0.0608991 |
0.2888077 |
0.3170652 |
0.3825778 |
0.522001 |
0.4995234 |
0.6087119 |
0.7762392 |
0.905863 |
1.0473846 |
1.1412954 |
0.7748437 |
| 0.00002 |
2.724E-05 |
0.000196 |
0.0015998 |
0.0096503 |
0.0713962 |
0.2513189 |
0.3160284 |
0.3950491 |
0.466485 |
0.5382551 |
0.6527739 |
0.8144579 |
0.9459105 |
1.108404 |
1.2340279 |
0.8880342 |
| 0.00003 |
4.075E-05 |
0.0003282 |
0.00184 |
0.0109043 |
0.0538215 |
0.268653 |
0.3198481 |
0.3931039 |
0.4887558 |
0.5861713 |
0.6808446 |
0.8626258 |
0.9753595 |
1.1600875 |
1.3038467 |
0.6251913 |
| 0.00004 |
4.913E-05 |
0.0003214 |
0.0023298 |
0.0125392 |
0.0614902 |
0.2813988 |
0.3429292 |
0.4090793 |
0.5199579 |
0.6105304 |
0.7378914 |
0.8493284 |
0.9988075 |
1.0854654 |
1.0873 |
0.8729225 |
| 0.00005 |
5.994E-05 |
0.0003756 |
0.0023409 |
0.0117232 |
0.062202 |
0.3072184 |
0.3516385 |
0.4274127 |
0.5169479 |
0.6399569 |
0.7341891 |
0.8889527 |
1.0051421 |
1.1449926 |
1.2100571 |
0.7909783 |
| 0.00006 |
7.661E-05 |
0.0004322 |
0.0028442 |
0.0137929 |
0.0656338 |
0.3209259 |
0.3746969 |
0.4383802 |
0.5358001 |
0.6555049 |
0.7775587 |
0.8993325 |
1.0583921 |
1.1989209 |
1.1462423 |
0.8956167 |
| 0.00007 |
7.451E-05 |
0.0004615 |
0.0030066 |
0.0144225 |
0.0633579 |
0.3240263 |
0.3882018 |
0.464098 |
0.5558809 |
0.6531999 |
0.8005515 |
0.9364608 |
1.1294862 |
1.1033066 |
1.2717927 |
0.8856261 |
| 0.00008 |
0.0001012 |
0.0005796 |
0.0032257 |
0.0157379 |
0.0735473 |
0.3464736 |
0.3998208 |
0.4841465 |
0.5697907 |
0.6867112 |
0.8156081 |
0.9274135 |
1.0616439 |
1.2542375 |
1.1973752 |
0.8678957 |
| 0.00009 |
0.000111 |
0.0005815 |
0.0031617 |
0.0158506 |
0.0761989 |
0.3575832 |
0.4293997 |
0.4879561 |
0.5507438 |
0.6942849 |
0.8239491 |
0.9702929 |
1.1280595 |
1.2025665 |
1.3201975 |
0.740521 |
| 1.00E-04 |
0.0001224 |
0.0006639 |
0.0034651 |
0.0159793 |
0.0725392 |
0.3658985 |
0.4247149 |
0.5038434 |
0.5942465 |
0.6933462 |
0.8483976 |
0.9721623 |
1.0693771 |
1.145319 |
1.2327502 |
0.7538966 |
| 1.00E-03 |
0.0017112 |
0.0064894 |
0.0210157 |
0.0665474 |
0.2281123 |
0.7468663 |
0.8383795 |
0.9688516 |
1.1025719 |
1.2164634 |
1.3232397 |
1.4956472 |
1.6227926 |
1.5731495 |
1.7120347 |
0.9489721 |
| 1.00E-02 |
0.0351804 |
0.0747061 |
0.185441 |
0.3578424 |
0.8277091 |
1.8117901 |
1.9827066 |
2.0461634 |
2.1930499 |
2.4045262 |
2.4932075 |
2.4621519 |
2.7222608 |
2.3760928 |
2.0409083 |
1.4589545 |
| 0.1 |
0.7188402 |
1.0194306 |
1.4336263 |
2.1769219 |
3.3319261 |
4.5258408 |
4.4975991 |
4.5914171 |
4.4601245 |
4.5623825 |
4.2728428 |
4.4018408 |
4.1001783 |
4.2275844 |
3.8980204 |
1.9917939 |
这个表达式在r∈（1e-3,0.1）, δ<0.1的区间上拟合度相对较高
但当r>0.1时计算值严重偏大。
然后统计r对网络准确率的影响，网络的收敛准确率通过相应迭代次数的平均准确率p-ave和相应迭代次数内最大准确率p-max来衡量
首先观察p-ave
| p-ave |
0.6 |
0.5 |
0.4 |
0.3 |
0.2 |
0.1 |
9*1e-2 |
8*1e-2 |
7*1e-2 |
6*1e-2 |
5*1e-2 |
4*1e-2 |
3*1e-2 |
2*1e-2 |
1.00E-02 |
1.00E-03 |
| 0.1 |
0.50532161 |
0.512315 |
0.55935097 |
0.66877021 |
0.76173263 |
0.87929672 |
0.89233943 |
0.90860865 |
0.92286684 |
0.92179248 |
0.93402213 |
0.93078398 |
0.940439 |
0.94137805 |
0.94436015 |
0.9438922 |
| 1.00E-02 |
0.56657028 |
0.60515694 |
0.60707472 |
0.67544732 |
0.7051479 |
0.87987823 |
0.88326573 |
0.90184771 |
0.88934072 |
0.90783581 |
0.91791674 |
0.93556339 |
0.92807441 |
0.95058596 |
0.94728994 |
0.95565496 |
| 1.00E-03 |
0.64879912 |
0.69057875 |
0.733498 |
0.77050324 |
0.65825753 |
0.84748758 |
0.86600747 |
0.87455918 |
0.90579252 |
0.91133 |
0.91854429 |
0.92634596 |
0.94091573 |
0.94668829 |
0.96408392 |
0.96918489 |
| 1.00E-04 |
0.73310139 |
0.75963913 |
0.70722634 |
0.76651204 |
0.80636886 |
0.85403579 |
0.87125239 |
0.88150744 |
0.86584533 |
0.88891901 |
0.90533566 |
0.9330532 |
0.94589605 |
0.96858324 |
0.97504447 |
0.97487298 |
| 9*1e-5 |
0.7489658 |
0.70132237 |
0.74081772 |
0.73584252 |
0.81142438 |
0.85867545 |
0.86737365 |
0.87424948 |
0.88887885 |
0.90593811 |
0.91912665 |
0.94061389 |
0.95362519 |
0.96531338 |
0.9640316 |
0.97271924 |
| 8*1e-5 |
0.74331285 |
0.70543908 |
0.70331044 |
0.71840673 |
0.82153543 |
0.86508946 |
0.87319051 |
0.86614484 |
0.86528807 |
0.87739723 |
0.91774103 |
0.93824427 |
0.94690023 |
0.96131108 |
0.97326567 |
0.97404462 |
| 7*1e-5 |
0.75671225 |
0.72010362 |
0.74226861 |
0.74230375 |
0.80382854 |
0.87211233 |
0.87682448 |
0.85890436 |
0.86777316 |
0.89462217 |
0.93143663 |
0.92926281 |
0.95006492 |
0.9686094 |
0.97088521 |
0.97459686 |
| 6*1e-5 |
0.72578669 |
0.74825793 |
0.73378416 |
0.77233568 |
0.81042533 |
0.87048211 |
0.87227639 |
0.85861714 |
0.89091712 |
0.90741912 |
0.92451352 |
0.93214953 |
0.95078509 |
0.96191273 |
0.97308256 |
0.97608792 |
| 5*1e-5 |
0.71897905 |
0.76718979 |
0.72594233 |
0.73235837 |
0.78668896 |
0.86196074 |
0.86631218 |
0.86140444 |
0.88416471 |
0.89293532 |
0.92339398 |
0.93949937 |
0.9579462 |
0.9585644 |
0.97378885 |
0.97818644 |
| 4*1e-5 |
0.75237966 |
0.73812177 |
0.76766673 |
0.78846115 |
0.78229612 |
0.87558151 |
0.85960618 |
0.86494101 |
0.89988353 |
0.90973352 |
0.92430267 |
0.93653232 |
0.94960847 |
0.96204353 |
0.97263786 |
0.97625359 |
| 3*1e-5 |
0.72364299 |
0.77646746 |
0.79820572 |
0.80097195 |
0.81758941 |
0.85857604 |
0.84173102 |
0.87581046 |
0.90257445 |
0.8940398 |
0.91045645 |
0.93777236 |
0.95656672 |
0.9586952 |
0.96952496 |
0.97735807 |
| 2*1e-5 |
0.77747153 |
0.78257224 |
0.79657911 |
0.79819065 |
0.8438661 |
0.83116054 |
0.83916601 |
0.86024057 |
0.87108661 |
0.91140029 |
0.93171777 |
0.93904251 |
0.95144439 |
0.96510411 |
0.97132992 |
0.97813121 |
| 1.00E-05 |
0.80439585 |
0.8260588 |
0.84255578 |
0.8722413 |
0.86864169 |
0.85205765 |
0.88819595 |
0.88038353 |
0.88893909 |
0.91370464 |
0.92190293 |
0.94263711 |
0.9479957 |
0.95987234 |
0.97473056 |
0.97978794 |
| 9*1e-6 |
0.79429986 |
0.84715445 |
0.83986485 |
0.8751481 |
0.87099624 |
0.88018141 |
0.87091271 |
0.89741451 |
0.8890897 |
0.91631524 |
0.92562303 |
0.9488674 |
0.95453808 |
0.96251439 |
0.97708486 |
0.97757897 |
| 8*1e-6 |
0.84018616 |
0.84747575 |
0.85380143 |
0.88311545 |
0.87336587 |
0.8850671 |
0.88862054 |
0.89324855 |
0.8977348 |
0.91219351 |
0.92635601 |
0.94067414 |
0.95422364 |
0.96910641 |
0.97235011 |
0.97708195 |
| 7*1e-6 |
0.83084824 |
0.83647609 |
0.8465269 |
0.88109223 |
0.882759 |
0.88239066 |
0.88980938 |
0.88980189 |
0.90761994 |
0.91615459 |
0.92238488 |
0.95210555 |
0.95565383 |
0.96918489 |
0.97687559 |
0.97619837 |
| 6*1e-6 |
0.84234492 |
0.85846035 |
0.86038818 |
0.91336325 |
0.90940719 |
0.89694334 |
0.88962956 |
0.89438994 |
0.91788662 |
0.90144487 |
0.93388156 |
0.93840492 |
0.94549032 |
0.96868787 |
0.97496599 |
0.97973271 |
| 5*1e-6 |
0.84611523 |
0.85919332 |
0.88181517 |
0.882759 |
0.90576742 |
0.91289761 |
0.89068853 |
0.91256981 |
0.92256562 |
0.90911099 |
0.94149748 |
0.94478583 |
0.95310788 |
0.96290677 |
0.97959611 |
0.9806163 |
| 4*1e-6 |
0.85797839 |
0.88415467 |
0.88681045 |
0.89816656 |
0.92276643 |
0.90874503 |
0.91872634 |
0.91516729 |
0.91866478 |
0.93705444 |
0.93703436 |
0.94456995 |
0.95761147 |
0.96722298 |
0.97582924 |
0.97890435 |
| 3*1e-6 |
0.8673163 |
0.87538406 |
0.90587284 |
0.90447718 |
0.91060204 |
0.92182654 |
0.9238189 |
0.93184611 |
0.93733056 |
0.93834468 |
0.93491074 |
0.94993674 |
0.96563476 |
0.97106833 |
0.97881134 |
|
| 2*1e-6 |
0.87372231 |
0.8951242 |
0.90702753 |
0.9239412 |
0.92749061 |
0.93123012 |
0.94029541 |
0.9443265 |
0.93957467 |
0.94891761 |
0.94483101 |
0.96033396 |
0.96208464 |
0.97080674 |
0.97664016 |
|
| 1.00E-06 |
0.891133 |
0.91104384 |
0.90327731 |
0.93696407 |
0.94718557 |
0.9513842 |
0.95146957 |
0.95735886 |
0.95650842 |
0.95672932 |
0.96055485 |
0.95794927 |
0.96976305 |
0.96915873 |
0.97475672 |
|
| 1.00E-07 |
0.93427817 |
0.97139025 |
0.97158007 |
0.97588208 |
0.9755658 |
0.97326646 |
0.97634898 |
0.9773299 |
规则明显
| δ |
r |
p-ave |
| 减小 |
不变 |
增大 |
| 不变 |
减小 |
增大 |
收敛标准δ和学习率r减小都有助于p-ave增大。
再观察r对p-max的影响
| p-max |
0.6 |
0.5 |
0.4 |
0.3 |
0.2 |
0.1 |
9*1e-2 |
8*1e-2 |
7*1e-2 |
6*1e-2 |
5*1e-2 |
4*1e-2 |
3*1e-2 |
2*1e-2 |
1.00E-02 |
1.00E-03 |
| 0.1 |
0.86928429 |
0.87176938 |
0.95029821 |
0.9582505 |
0.96719682 |
0.96819086 |
0.97316103 |
0.97166998 |
0.97017893 |
0.97365805 |
0.96819086 |
0.97266402 |
0.9666998 |
0.97117296 |
0.97166998 |
0.97117296 |
| 1.00E-02 |
0.96023857 |
0.95079523 |
0.96322068 |
0.97117296 |
0.9666998 |
0.97266402 |
0.97713718 |
0.972167 |
0.9776342 |
0.97564612 |
0.97713718 |
0.97664016 |
0.972167 |
0.97017893 |
0.97415507 |
0.96968191 |
| 1.00E-03 |
0.97415507 |
0.97067594 |
0.97664016 |
0.97117296 |
0.97664016 |
0.98011928 |
0.97813121 |
0.97465209 |
0.9776342 |
0.97862823 |
0.98011928 |
0.97316103 |
0.97614314 |
0.97465209 |
0.9776342 |
0.97564612 |
| 1.00E-04 |
0.97465209 |
0.98161034 |
0.97514911 |
0.98111332 |
0.97614314 |
0.98210736 |
0.97713718 |
0.98359841 |
0.98260437 |
0.98111332 |
0.9806163 |
0.98111332 |
0.98210736 |
0.98210736 |
0.98359841 |
0.98111332 |
| 9*1e-5 |
0.98210736 |
0.97614314 |
0.97912525 |
0.97514911 |
0.97664016 |
0.97862823 |
0.9806163 |
0.97962227 |
0.97664016 |
0.98011928 |
0.98409543 |
0.98111332 |
0.98260437 |
0.97962227 |
0.9806163 |
0.98161034 |
| 8*1e-5 |
0.9806163 |
0.97862823 |
0.97862823 |
0.98011928 |
0.97912525 |
0.98359841 |
0.98161034 |
0.97912525 |
0.97514911 |
0.98459245 |
0.98558648 |
0.98310139 |
0.98260437 |
0.98210736 |
0.98210736 |
0.97862823 |
| 7*1e-5 |
0.97813121 |
0.97664016 |
0.97713718 |
0.98111332 |
0.97862823 |
0.98210736 |
0.97962227 |
0.98161034 |
0.98011928 |
0.98260437 |
0.98161034 |
0.98210736 |
0.98260437 |
0.98111332 |
0.9806163 |
0.9806163 |
| 6*1e-5 |
0.97465209 |
0.97962227 |
0.97564612 |
0.98111332 |
0.9806163 |
0.98161034 |
0.9806163 |
0.98310139 |
0.98310139 |
0.97962227 |
0.98011928 |
0.98260437 |
0.98210736 |
0.97813121 |
0.98210736 |
0.97813121 |
| 5*1e-5 |
0.9776342 |
0.98161034 |
0.97912525 |
0.97465209 |
0.9776342 |
0.9806163 |
0.98310139 |
0.98260437 |
0.9806163 |
0.98161034 |
0.98111332 |
0.98409543 |
0.98409543 |
0.98161034 |
0.98111332 |
0.98459245 |
| 4*1e-5 |
0.97813121 |
0.97664016 |
0.9806163 |
0.97912525 |
0.98111332 |
0.98459245 |
0.98161034 |
0.98459245 |
0.98210736 |
0.98459245 |
0.98210736 |
0.98011928 |
0.98161034 |
0.98210736 |
0.98161034 |
0.98111332 |
| 3*1e-5 |
0.9776342 |
0.98359841 |
0.97813121 |
0.97862823 |
0.98260437 |
0.98161034 |
0.98310139 |
0.98210736 |
0.9806163 |
0.98260437 |
0.98161034 |
0.98210736 |
0.98658052 |
0.98210736 |
0.98011928 |
0.9806163 |
| 2*1e-5 |
0.98011928 |
0.98161034 |
0.98111332 |
0.98260437 |
0.9806163 |
0.97813121 |
0.9806163 |
0.98260437 |
0.97614314 |
0.98508946 |
0.98508946 |
0.98558648 |
0.98359841 |
0.98260437 |
0.98459245 |
0.98111332 |
| 1.00E-05 |
0.98260437 |
0.98260437 |
0.98260437 |
0.98161034 |
0.98459245 |
0.98260437 |
0.98409543 |
0.98409543 |
0.98459245 |
0.98508946 |
0.98558648 |
0.98508946 |
0.98310139 |
0.98260437 |
0.98459245 |
0.98310139 |
| 9*1e-6 |
0.98111332 |
0.98558648 |
0.98558648 |
0.98111332 |
0.98161034 |
0.98359841 |
0.98310139 |
0.98459245 |
0.98111332 |
0.98459245 |
0.98359841 |
0.98310139 |
0.98707754 |
0.98359841 |
0.98508946 |
0.98111332 |
| 8*1e-6 |
0.98111332 |
0.98161034 |
0.98111332 |
0.98161034 |
0.98359841 |
0.98260437 |
0.98359841 |
0.98161034 |
0.98260437 |
0.98310139 |
0.98409543 |
0.98409543 |
0.98658052 |
0.98409543 |
0.98508946 |
0.98260437 |
| 7*1e-6 |
0.98508946 |
0.98011928 |
0.98260437 |
0.98111332 |
0.98707754 |
0.98210736 |
0.98359841 |
0.98210736 |
0.98409543 |
0.98459245 |
0.98558648 |
0.9860835 |
0.98508946 |
0.98359841 |
0.98707754 |
0.97912525 |
| 6*1e-6 |
0.98359841 |
0.98161034 |
0.98459245 |
0.98459245 |
0.98459245 |
0.98409543 |
0.98459245 |
0.98508946 |
0.98210736 |
0.98161034 |
0.98757455 |
0.98310139 |
0.98359841 |
0.98409543 |
0.98409543 |
0.98359841 |
| 5*1e-6 |
0.98459245 |
0.97912525 |
0.98359841 |
0.98260437 |
0.98558648 |
0.98757455 |
0.98707754 |
0.98459245 |
0.98558648 |
0.98409543 |
0.98260437 |
0.98459245 |
0.98359841 |
0.98409543 |
0.98658052 |
0.98310139 |
| 4*1e-6 |
0.98210736 |
0.98359841 |
0.98658052 |
0.98409543 |
0.98508946 |
0.9860835 |
0.98210736 |
0.9860835 |
0.98508946 |
0.98409543 |
0.98558648 |
0.98658052 |
0.98260437 |
0.9860835 |
0.98508946 |
0.98260437 |
| 3*1e-6 |
0.98210736 |
0.97962227 |
0.98409543 |
0.98508946 |
0.9860835 |
0.9860835 |
0.98310139 |
0.98558648 |
0.98757455 |
0.98359841 |
0.98658052 |
0.98459245 |
0.9860835 |
0.98508946 |
0.98658052 |
|
| 2*1e-6 |
0.98310139 |
0.98310139 |
0.9806163 |
0.98558648 |
0.98658052 |
0.98558648 |
0.98707754 |
0.98508946 |
0.98409543 |
0.98459245 |
0.9860835 |
0.9860835 |
0.9860835 |
0.98310139 |
0.98508946 |
|
| 1.00E-06 |
0.98508946 |
0.98558648 |
0.98707754 |
0.9860835 |
0.98558648 |
0.98558648 |
0.98658052 |
0.98658052 |
0.98558648 |
0.98558648 |
0.9860835 |
0.98459245 |
0.98359841 |
0.98459245 |
0.98558648 |
|
| 1.00E-07 |
0.98558648 |
0.99005964 |
0.98956262 |
0.98956262 |
0.98856859 |
0.98658052 |
0.9860835 |
0.98906561 |
0.98558648 |
| δ |
r |
p-max |
| 减小 |
不变 |
增大 |
| 不变 |
减小 |
变化不大甚至有减小趋势 |
在学习率r不变的情况下δ越小p-max越大，但在一定收敛标准δ的情况下，学习率r减小并不一定会提升准确率。
比如取δ=1e-6的数据画成图
明显的随着r减小，p-max是减小的。
所以学习率r对网络收敛准确率的影响可以概括成，如果比较在意网络的平均性能，希望网络每次收敛都一个相对较高的准确率应该让r尽可能的小，r减小虽然迭代次数也会增加网络收敛速度变慢，但可以使网络的性能变得更加稳定可期待。
但是如果更在意网络的收敛效率应该选择一个更大的学习率
| p-max |
0.5 |
0.4 |
0.3 |
0.2 |
0.1 |
9*1e-2 |
8*1e-2 |
7*1e-2 |
6*1e-2 |
5*1e-2 |
4*1e-2 |
| 1.00E-06 |
0.985586 |
0.987078 |
0.986083 |
0.985586 |
0.985586 |
0.986581 |
0.986581 |
0.985586 |
0.985586 |
0.986083 |
0.984592 |
| 规定迭代次数 |
99 |
99 |
99 |
99 |
199 |
199 |
199 |
99 |
99 |
99 |
99 |
| 耗时ms/199 |
1493995 |
1690726 |
2116256 |
2336894 |
6053900 |
6426435 |
3767392 |
3938233 |
4076070 |
3874740 |
|
| 耗时min/199 |
24.89992 |
28.17877 |
35.27093 |
38.94823 |
118.6276 |
100.8983 |
107.1073 |
62.78987 |
65.63722 |
67.9345 |
64.579 |
比如当r=0.5时可以在24min将网络收敛到1e-6共99次，这99次中的最大准确率为0.985586，而当r=0.1的时候将网络收敛到1e-6在118min内能完成199次，最大准确率同样也是0.985586，也就表明在更看重收敛效率的情况下选择一个较大的学习率可能在相对短的时间里面得到一个相对较大的准确率。
实验数据
| 学习率 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 |
曲线拟合
http://www.qinms.com/webapp/curvefit/cf.aspx
|
0.6 |
1.00E-06,0.000002,0.000003,0.000004,0.000005,0.000006,0.000007,0.000008,0.000009,1.00E-05,0.00002,0.00003,0.00004,0.00005,0.00006,0.00007,0.00008,0.00009,1.00E-04 |
| 44776.82828,33399.28283,22927.21212,22163.83838,15435.36364,16363.42424,15447.69697,12061.33333,14148.73737,10469.17172,6345.707071,5155.454545,4030.030303,3513.757576,3412.848485,2631.686869,2923.363636,2685.737374,2526.929293 |
|
| 拟合方程式：y=ax^b |
|
| a = 5.35983868500963 |
|
| b = -0.661586726096118 |
|
| 相关系数 R2：0.979033223519759 |
|
| 0.5 |
1.00E-06,0.000002,0.000003,0.000004,0.000005,0.000006,0.000007,0.000008,0.000009,1.00E-05,0.00002,0.00003,0.00004,0.00005,0.00006,0.00007,0.00008,0.00009,1.00E-04 |
| 49352.59596,36129.63636,28859.89899,27614.27273,22017.15152,19538.9798,17055.31313,16990.62626,15900.17172,12805.16162,6690.979798,6595.010101,4433.727273,3871.20202,3509.919192,3063.919192,3232.070707,2779.69697,2765.444444 |
|
| 拟合方程式：y=ax^b |
|
| a = 4.67754840973275 |
|
| b = -0.687119998641929 |
|
| 相关系数 R2：0.937377453678227 |
|
| 0.4 |
1.00E-06,0.000002,0.000003,0.000004,0.000005,0.000006,0.000007,0.000008,0.000009,1.00E-05,0.00002,0.00003,0.00004,0.00005,0.00006,0.00007,0.00008,0.00009,1.00E-04 |
| 58196.59596,41347.37374,36659.58586,32165.78788,28748.13131,23098.06061,16756.80808,17342.90909,14248.55556,14396.63636,8425.636364,6183.333333,5692.383838,4466.686869,4434.292929,3951.424242,3656.383838,3145.353535,3067.30303 |
|
| 拟合方程式：y=ax^b |
|
| a = 5.52390567202631 |
|
| b = -0.684159942010868 |
|
| 相关系数 R2：0.948243801142418 |
|
| 0.3 |
1.00E-06,0.000002,0.000003,0.000004,0.000005,0.000006,0.000007,0.000008,0.000009,1.00E-05,0.00002,0.00003,0.00004,0.00005,0.00006,0.00007,0.00008,0.00009,1.00E-04 |
| 66137.77778,47989.30303,35043.41414,35140.71717,27564.82828,23525.24242,23393.78788,20878.49495,20380.20202,16427.84848,8510.676768,6651.494949,5888.414141,4494.323232,4480.060606,4071.959596,3935.353535,3561.020202,3262.010101 |
|
| 拟合方程式：y=ax^b |
|
| a = 5.01930229957708 |
|
| b = -0.700931762463355 |
|
| 相关系数 R2：0.951701726538571 |
|
| 0.2 |
1.00E-06,0.000002,0.000003,0.000004,0.000005,0.000006,0.000007,0.000008,0.000009,1.00E-05,0.00002,0.00003,0.00004,0.00005,0.00006,0.00007,0.00008,0.00009,1.00E-04 |
| 74129.0202,54458.32323,42120.18182,38190.20202,27952.26263,25262.9596,24626.9596,18792.87879,18224.71717,17083.18182,12240.60606,6918.323232,6443.232323,5562.424242,5156.353535,4461.30303,4710.141414,4488.292929,3964.626263 |
|
| 拟合方程式：y=ax^b |
|
| a = 7.53120014322965 |
|
| b = -0.673994166670858 |
|
| 相关系数 R2：0.979851362401591 |
|
| 0.1 |
1.00E-06,0.000002,0.000003,0.000004,0.000005,0.000006,0.000007,0.000008,0.000009,1.00E-05,0.00002,0.00003,0.00004,0.00005,0.00006,0.00007,0.00008,0.00009,1.00E-04 |
| 90392.445,60366.615,45173.585,37281.575,33681.76,28410.335,28045.605,22214.925,22245.54,19757.36,12060.835,10478,9473.415,9227.095,8780.59,8193.28,8182.51,7951.16,7709.22 |
|
| 拟合方程式：y=ax^b |
|
| a = 44.4044340884289 |
|
| b = -0.540714410219626 |
|
| 相关系数 R2：0.970788788627853 |
|
| 9*1e-2 |
1.00E-06,0.000002,0.000003,0.000004,0.000005,0.000006,0.000007,0.000008,0.000009,1.00E-05,0.00002,0.00003,0.00004,0.00005,0.00006,0.00007,0.00008,0.00009,1.00E-04 |
| 100523.6633,70687.12563,51076.76884,40560.41206,32414.69849,30478.00503,26621.37688,24338.42714,22123.03518,19716.13065,13977.11558,11589.68342,10787.33668,9912.125628,9656.643216,9274.567839,8945.271357,9066.592965,8515.030151 |
|
| 拟合方程式：y=ax^b |
|
| a = 52.9869736440654 |
|
| b = -0.532045068079058 |
|
| 相关系数 R2：0.943066820020848 |
|
| 8*1e-2 |
1.00E-06,0.000002,0.000003,0.000004,0.000005,0.000006,0.000007,0.000008,0.000009,1.00E-05,0.00002,0.00003,0.00004,0.00005,0.00006,0.00007,0.00008,0.00009,1.00E-04 |
| 110144.4523,73467.41709,54023.83417,38845.31658,34133.28141,30443.50754,27583.9397,23525.55779,23800.66332,21967.58291,16357.54774,13443.50251,12214.59799,11487.00503,10810.86935,10642.42714,10424.44724,9938.673367,9764.713568 |
|
| 拟合方程式：y=ax^b |
|
| a = 79.3951192582378 |
|
| b = -0.503223333087072 |
|
| 相关系数 R2：0.908628657384572 |
|
| 7*1e-2 |
1.00E-06,0.000002,0.000003,0.000004,0.000005,0.000006,0.000007,0.000008,0.000009,1.00E-05,0.00002,0.00003,0.00004,0.00005,0.00006,0.00007,0.00008,0.00009,1.00E-04 |
| 103614.2222,65498.10101,56240.52525,49073,39291.53535,34617.61616,27712.88889,26229.38384,24885.10101,28235.14141,18447.90909,16093.15152,15033.78788,13513.31313,12898.60606,12481.71717,12044.9899,11038.9899,11357.23232 |
|
| 拟合方程式：y=ax^b |
|
| a = 136.397227804853 |
|
| b = -0.464943988983266 |
|
| 相关系数 R2：0.943840009909165 |
|
| 6*1e-2 |
1.00E-06,0.000002,0.000003,0.000004,0.000005,0.000006,0.000007,0.000008,0.000009,1.00E-05,0.00002,0.00003,0.00004,0.00005,0.00006,0.00007,0.00008,0.00009,1.00E-04 |
| 111489.7374,79889.05051,55068.74747,49934.84848,39831.37374,36609.33333,33021.67677,30248.33333,30206.63636,26126.29293,20868.30303,19075.0202,17546.21212,16702.0101,15812.24242,14741.69697,14629.42424,14057.13131,13413.59596 |
|
| 拟合方程式：y=ax^b |
|
| a = 230.866589709944 |
|
| b = -0.429044180199772 |
|
| 相关系数 R2：0.914564237405916 |
|
| 5*1e-2 |
1.00E-06,0.000002,0.000003,0.000004,0.000005,0.000006,0.000007,0.000008,0.000009,1.00E-05,0.00002,0.00003,0.00004,0.00005,0.00006,0.00007,0.00008,0.00009,1.00E-04 |
| 127626.5859,84106.93939,61498.83838,56060.85859,49829.57576,43348.80808,40300.85859,37264.59596,36951.22222,31903.63636,25713.10101,22692.52525,21844.76768,19825.85859,19477.41414,18819.26263,18146.78788,17463.91919,17218.16162 |
|
| 拟合方程式：y=ax^b |
|
| a = 369.071059613474 |
|
| b = -0.403561618090458 |
|
| 相关系数 R2：0.917851616709726 |
|
| 4*1e-2 |
1.00E-06,0.000002,0.000003,0.000004,0.000005,0.000006,0.000007,0.000008,0.000009,1.00E-05,0.00002,0.00003,0.00004,0.00005,0.00006,0.00007,0.00008,0.00009,1.00E-04 |
| 135720.404,99886.30303,72600.90909,59055.56566,57589.24242,51335.07071,48154.71717,45743.9798,45709.41414,42933.43434,34325.60606,31011.08081,27275.62626,26156.16162,24635.66667,24147.93939,22694.57576,22672.10101,21796.34343 |
|
| 拟合方程式：y=ax^b |
|
| a = 679.834517164632 |
|
| b = -0.367433327780221 |
|
| 相关系数 R2：0.928700468687962 |
|
| 3*1e-2 |
1.00E-06,0.000002,0.000003,0.000004,0.000005,0.000006,0.000007,0.000008,0.000009,1.00E-05,0.00002,0.00003,0.00004,0.00005,0.00006,0.00007,0.00008,0.00009,1.00E-04 |
| 159008.3061,110986.5306,94621.93878,80945.44898,79256.63265,72964.42857,66872.63265,63118.79592,61453.44898,57378.77551,46288.65306,41042.69388,37760.87755,34971.2449,34407.53061,34670.93878,31008.08163,31534.46939,28744.20408 |
|
| 拟合方程式：y=ax^b |
|
| a = 1196.15736585087 |
|
| b = -0.342974299278252 |
|
| 相关系数 R2：0.969864964457347 |
|
| 2*1e-2 |
1.00E-06,0.000002,0.000003,0.000004,0.000005,0.000006,0.000007,0.000008,0.000009,1.00E-05,0.00002,0.00003,0.00004,0.00005,0.00006,0.00007,0.00008,0.00009,1.00E-04 |
| 232212.3158,195190,130338.4211,105719.4211,112888.3158,101187.3158,91680.15789,102146.8947,99080.42105,88206.84211,73116.63158,66337.21053,56086.21053,54688.15789,53700.57895,46805.15789,50762.36842,46692.36842,42848.84211 |
|
| 拟合方程式：y=ax^b |
|
| a = 1962.58276618837 |
|
| b = -0.335023592346894 |
|
| 相关系数 R2：0.932282237845563 |
|
| 1.00E-02 |
1.00E-06,0.000002,0.000003,0.000004,0.000005,0.000006,0.000007,0.000008,0.000009,1.00E-05,0.00002,0.00003,0.00004,0.00005,0.00006,0.00007,0.00008,0.00009,1.00E-04 |
| 477003.3158,346350.4737,311185.9474,235798.3158,231381.3684,235256.1579,227748.6842,238433.6316,197259.9474,184000.6842,157999.1053,145883.5263,110557.2632,114240.8421,101850.7895,107360.5263,96688.84211,102512.5263,92427 |
|
| 拟合方程式：y=ax^b |
|
| a = 3954.43474850769 |
|
| b = -0.339945741760316 |
|
| 相关系数 R2：0.973311534518584 |
|
| 1.00E-03 |
0.000004,0.000005,0.000006,0.000007,0.000008,0.000009,1.00E-05,0.00002,0.00003,0.00004,0.00005,0.00006,0.00007,0.00008,0.00009,1.00E-04 |
| 2647137.778,2399699.889,2245382.333,2427938.556,2161505.667,1782400,1880675.111,1733106.333,1074014.333,1369831.333,1157092.444,1237129.556,1165418.556,1095102.556,900392.7778,886770.2222 |
|
| 拟合方程式：y=ax^b |
|
| a = 53754.1569229673 |
|
| b = -0.311864962861574 |
|
| 相关系数 R2：0.942765543229613 |
f2[0] f2[1] 迭代次数n 准确率 δ 耗时ms/次
0.056513844 0.943526975 606 0.487077535 0.1 1418
0.906284729 0.088474273 331 0.487077535 0.1 921
0.937198922 0.062371492 667 0.487077535 0.1 913
0.932882338 0.065543624 331 0.487077535 0.1 815
0.929872401 0.069967933 217 0.487077535 0.1 718
0.909411712 0.090891359 709 0.487077535 0.1 870
0.06718993 0.932412004 208 0.487077535 0.1 710
0.938554679 0.060950597 249 0.487077535 0.1 733
0.902338886 0.096338414 1269 0.487077535 0.1 1070
0.092412814 0.906806913 510 0.487077535 0.1 790
0.924237765 0.076149926 569 0.487077535 0.1 802
0.925417989 0.076429729 219 0.487077535 0.1 731
0.911428203 0.093231305 251 0.487077535 0.1 838
0.094925045 0.905916028 290 0.487077535 0.1 725
0.933807158 0.062023017 369 0.487077535 0.1 745
0.088488307 0.910995566 276 0.496520875 0.1 736
0.908623398 0.094631694 387 0.487077535 0.1 819
0.06950962 0.92795209 420 0.613817097 0.1 839
0.912370388 0.085621325 481 0.487077535 0.1 846
0.90077696 0.094248222 193 0.487077535 0.1 719
0.908921457 0.09445952 607 0.487077535 0.1 816
0.063464298 0.934038708 420 0.487077535 0.1 775
0.929475876 0.075005228 265 0.487077535 0.1 724
0.908180495 0.088303262 839 0.487077535 0.1 889
0.051984 0.94581641 276 0.635685885 0.1 728
0.074180498 0.925794015 276 0.687872763 0.1 732
0.08920808 0.909986051 536 0.487077535 0.1 802
0.911595271 0.087774763 303 0.487077535 0.1 727
0.919276923 0.083610071 291 0.487077535 0.1 725
0.079297726 0.921290606 708 0.487077535 0.1 857
0.950217501 0.049441852 743 0.487077535 0.1 851
0.908526181 0.092436541 677 0.487077535 0.1 841
0.908545693 0.097347776 291 0.487077535 0.1 763
0.064526875 0.935061646 496 0.49304175 0.1 852
0.090837984 0.912948071 394 0.487077535 0.1 789
0.907211554 0.090135457 953 0.487077535 0.1 912
0.93770582 0.061523379 335 0.50944334 0.1 740
0.919751343 0.082019291 417 0.487077535 0.1 762
0.048450687 0.95133849 276 0.487077535 0.1 719
0.927563314 0.068493598 607 0.487077535 0.1 827
0.913522192 0.086916532 303 0.487077535 0.1 725
0.920753423 0.078833445 249 0.487077535 0.1 721
0.911969965 0.083319077 273 0.487077535 0.1 735
0.02361007 0.976001076 254 0.73359841 0.1 711
0.92932522 0.072973838 331 0.487077535 0.1 741
0.042538303 0.959763451 276 0.532306163 0.1 725
0.072618821 0.926417703 254 0.489065606 0.1 711
0.93017768 0.07144937 467 0.487077535 0.1 781
0.097205263 0.902641667 742 0.487077535 0.1 851
0.079125768 0.919028321 514 0.487574553 0.1 797
0.913201874 0.084834793 619 0.487077535 0.1 838
0.085696133 0.915855596 276 0.494035785 0.1 719
0.941018929 0.058099879 627 0.487077535 0.1 836
0.908476848 0.094881198 369 0.487077535 0.1 744
0.08079389 0.925420889 750 0.499502982 0.1 864
0.915605405 0.085178204 795 0.487077535 0.1 875
0.927462726 0.071863671 373 0.487077535 0.1 745
0.075009731 0.9270274 420 0.526341948 0.1 792
0.905869391 0.092098351 569 0.487077535 0.1 808
0.967354602 0.034825844 331 0.487077535 0.1 736
0.955006783 0.045063552 689 0.487077535 0.1 840
0.90920423 0.091139444 743 0.487077535 0.1 850
0.906781999 0.092224078 335 0.502485089 0.1 746
0.093673639 0.906814991 420 0.487077535 0.1 764
0.079128871 0.925410761 254 0.869284294 0.1 746
0.909947354 0.089012348 669 0.487077535 0.1 847
0.05544754 0.944308838 654 0.487077535 0.1 824
0.082563321 0.917144677 254 0.827037773 0.1 719
0.078813513 0.9175504 520 0.487574553 0.1 792
0.901186775 0.094570869 325 0.487077535 0.1 732
0.908476325 0.090724356 339 0.487077535 0.1 750
0.914268728 0.083291383 655 0.487077535 0.1 855
0.911213346 0.089146523 387 0.487077535 0.1 764
0.934738092 0.064535646 331 0.487077535 0.1 737
0.919426534 0.08135671 283 0.487077535 0.1 722
0.900063 0.098106113 619 0.487077535 0.1 827
0.91760296 0.082640456 331 0.487077535 0.1 744
0.902690617 0.098556408 283 0.487077535 0.1 725
0.911160853 0.088838363 339 0.487077535 0.1 765
0.901809351 0.098956068 269 0.487077535 0.1 714
0.902855344 0.096867963 303 0.487077535 0.1 735
0.072796451 0.928515602 290 0.487077535 0.1 728
0.079189827 0.920370208 514 0.487077535 0.1 785
0.901974383 0.098668923 651 0.487077535 0.1 844
0.094007926 0.907406288 342 0.487077535 0.1 765
0.032213577 0.966972257 420 0.493538767 0.1 762
0.914822158 0.08723814 335 0.487077535 0.1 742
0.915522558 0.081207672 655 0.487077535 0.1 830
0.901566079 0.094417875 331 0.490059642 0.1 747
0.907816372 0.094147292 757 0.487077535 0.1 870
0.073322788 0.9241578 322 0.487077535 0.1 757
0.08033337 0.921266158 626 0.487077535 0.1 821
0.909045084 0.089717698 453 0.487077535 0.1 774
0.971469569 0.028431256 675 0.487077535 0.1 841
0.048519335 0.950353836 524 0.487077535 0.1 795
0.070420698 0.930137971 420 0.574552684 0.1 811
0.079657444 0.918234291 402 0.493538767 0.1 844
0.922163991 0.073352015 233 0.58499006 0.1 720
0.922287423 0.081170484 637 0.487077535 0.1 837
78519
0.003201299 0.996854494 926 0.531809145 0.01 914
0.990523638 0.009515195 733 0.487077535 0.01 874
0.006583561 0.993538682 420 0.719681909 0.01 766
0.003898516 0.996224547 930 0.501988072 0.01 909
0.990160622 0.009906422 417 0.625248509 0.01 779
0.995815339 0.004034719 607 0.487574553 0.01 820
0.994541309 0.005320455 935 0.489065606 0.01 943
0.990653862 0.009274768 927 0.487077535 0.01 910
0.009622431 0.990359781 496 0.497514911 0.01 789
0.996141577 0.003874047 825 0.487077535 0.01 881
0.991158126 0.008727789 825 0.487077535 0.01 887
0.990550604 0.009661975 313 0.487077535 0.01 745
0.998269387 0.001702698 733 0.491550696 0.01 895
0.00719275 0.992744229 524 0.532306163 0.01 818
0.007892627 0.992130844 944 0.538270378 0.01 958
0.992434735 0.007690597 429 0.691848907 0.01 767
0.007956452 0.992025414 528 0.5472167 0.01 799
0.996777594 0.003246138 927 0.487077535 0.01 926
0.007978646 0.991798215 528 0.711729622 0.01 852
0.990329165 0.009528363 453 0.487574553 0.01 840
0.990500305 0.009585676 825 0.487077535 0.01 887
0.009525781 0.990816034 2008 0.487077535 0.01 1220
0.00509626 0.994692679 420 0.855367793 0.01 798
0.009629043 0.990161698 528 0.513916501 0.01 788
0.00169608 0.998294582 420 0.877733598 0.01 763
0.994163089 0.005911322 543 0.490059642 0.01 800
0.992720582 0.007417125 655 0.561133201 0.01 833
0.992184008 0.008002502 783 0.499005964 0.01 872
0.996369204 0.003564631 655 0.49055666 0.01 827
0.006880053 0.993079656 420 0.587972167 0.01 774
0.004933349 0.995154636 998 0.497514911 0.01 929
0.993138609 0.006853029 733 0.499502982 0.01 845
0.990057681 0.009891843 743 0.509940358 0.01 863
0.00444927 0.995553662 708 0.602882704 0.01 847
0.990677816 0.009270798 1463 0.602882704 0.01 1075
0.994536938 0.005399457 487 0.487077535 0.01 777
0.992308449 0.007750139 949 0.487077535 0.01 926
0.992043392 0.008142537 607 0.487077535 0.01 813
0.995035883 0.004675228 825 0.487077535 0.01 894
0.006276075 0.993765756 420 0.574055666 0.01 766
0.008254957 0.991614353 676 0.585487078 0.01 830
0.007806494 0.992211282 838 0.489562624 0.01 897
0.99088997 0.009245669 521 0.597912525 0.01 789
0.991030086 0.009091853 487 0.49055666 0.01 782
0.007627216 0.992160472 496 0.752982107 0.01 792
0.005368742 0.994666345 708 0.701789264 0.01 841
0.992323968 0.008042675 413 0.526341948 0.01 764
0.995047955 0.0049969 733 0.487077535 0.01 854
0.994186185 0.005875203 743 0.491053678 0.01 860
0.992817711 0.007159374 927 0.546222664 0.01 903
0.00941793 0.990433575 420 0.541252485 0.01 768
0.990936905 0.009245819 647 0.566600398 0.01 837
0.004117811 0.99582039 926 0.695328032 0.01 933
0.004455386 0.995508788 644 0.604870775 0.01 834
0.991501615 0.008501645 689 0.49055666 0.01 845
0.993628976 0.006326126 733 0.487077535 0.01 852
0.00921364 0.990804519 772 0.776838966 0.01 861
0.005238561 0.99463298 496 0.852882704 0.01 784
0.006754177 0.993095845 1208 0.487077535 0.01 990
0.998717746 0.001271384 821 0.487077535 0.01 904
0.992686276 0.007177712 825 0.487077535 0.01 909
0.002695625 0.997356596 842 0.560139165 0.01 919
0.009735984 0.990355246 532 0.730119284 0.01 828
0.994279668 0.005646268 747 0.689860835 0.01 871
0.991282033 0.009059355 417 0.516898608 0.01 771
0.002839509 0.997206095 420 0.597912525 0.01 774
0.009654825 0.990240625 1018 0.897614314 0.01 954
0.004217898 0.995892921 420 0.846918489 0.01 797
0.990423734 0.009633014 1125 0.488568588 0.01 982
0.001751242 0.99819975 420 0.821570577 0.01 788
0.992263723 0.007656071 935 0.487077535 0.01 930
0.992112385 0.007994245 487 0.488071571 0.01 822
0.991004824 0.009062239 783 0.487077535 0.01 888
0.993983733 0.005938177 1015 0.488071571 0.01 938
0.008047008 0.992014774 886 0.507455268 0.01 923
0.995258744 0.004793777 655 0.511431412 0.01 834
0.995544817 0.00443916 825 0.487077535 0.01 900
0.99499645 0.005292772 453 0.489562624 0.01 772
0.994613994 0.005401848 689 0.487077535 0.01 891
0.991601628 0.008257278 453 0.504473161 0.01 785
0.990713856 0.009344664 419 0.566600398 0.01 784
0.993699017 0.006097255 331 0.487077535 0.01 764
0.006855145 0.993281094 998 0.487077535 0.01 945
0.990596767 0.009375544 777 0.61332008 0.01 876
0.99431349 0.005776523 1119 0.487077535 0.01 1008
0.00876883 0.991048108 684 0.628727634 0.01 865
0.993809805 0.006131658 835 0.595427435 0.01 911
0.992581474 0.007320914 487 0.487077535 0.01 823
0.009151199 0.990870652 504 0.842942346 0.01 833
0.004319966 0.995483594 290 0.612326044 0.01 726
0.993490483 0.006437905 1463 0.514413519 0.01 1098
0.007248081 0.992921397 772 0.487574553 0.01 868
0.009830764 0.990178223 456 0.754473161 0.01 783
0.007619795 0.991949636 420 0.524353877 0.01 778
0.994972328 0.005028381 1111 0.487077535 0.01 970
0.005545273 0.99455508 606 0.960238569 0.01 829
0.991246196 0.008863574 825 0.487077535 0.01 873
0.993863018 0.006050425 637 0.487077535 0.01 839
0.991080644 0.0089508 825 0.487077535 0.01 883
85023
2.69E-04 0.999729789 1140 0.811630219 0.001 974
1.24E-04 0.999880449 1140 0.959244533 0.001 975
4.98E-04 0.999511266 872 0.90805169 0.001 896
9.70E-04 0.999007606 708 0.97415507 0.001 850
0.99914927 8.44E-04 1111 0.488071571 0.001 960
0.999825556 1.74E-04 949 0.509940358 0.001 922
7.23E-04 0.99927703 1502 0.521868787 0.001 1076
0.999241099 7.66E-04 935 0.612326044 0.001 917
0.999108326 9.12E-04 655 0.487077535 0.001 827
0.999015657 9.94E-04 1883 0.489562624 0.001 1201
0.99937474 6.23E-04 821 0.487077535 0.001 881
7.40E-04 0.999245623 1140 0.715705765 0.001 975
0.999116534 8.98E-04 1917 0.623757455 0.001 1200
9.38E-04 0.999074393 1122 0.952286282 0.001 973
9.79E-04 0.999043512 4044 0.826540755 0.001 1824
5.46E-04 0.99944655 528 0.790755467 0.001 796
0.999736111 2.65E-04 935 0.532306163 0.001 922
8.04E-04 0.999173394 1286 0.914015905 0.001 1012
9.71E-05 0.999901131 1908 0.614811133 0.001 1204
0.999211617 7.86E-04 949 0.559642147 0.001 915
0.9993402 6.34E-04 949 0.550198807 0.001 924
0.999062687 9.49E-04 675 0.661033797 0.001 839
0.999507557 4.82E-04 1125 0.487574553 0.001 972
0.999297759 7.15E-04 949 0.503479125 0.001 930
0.999049386 9.57E-04 821 0.487077535 0.001 882
0.9992983 6.97E-04 949 0.520377734 0.001 918
9.85E-04 0.999036151 568 0.896620278 0.001 807
7.38E-04 0.999254659 930 0.890656064 0.001 911
0.999571113 4.26E-04 669 0.547713718 0.001 854
4.25E-04 0.999573081 2022 0.909045726 0.001 1233
0.999214782 8.08E-04 1249 0.487077535 0.001 1019
3.02E-04 0.999697029 930 0.789264414 0.001 915
9.53E-04 0.999109574 1710 0.893638171 0.001 1136
0.999286235 7.04E-04 655 0.802186879 0.001 830
6.24E-04 0.999385237 838 0.795725646 0.001 882
6.38E-04 0.999338893 838 0.731610338 0.001 905
0.999115332 8.86E-04 1119 0.523359841 0.001 976
0.999384858 6.20E-04 949 0.487574553 0.001 939
8.57E-04 0.99914883 1252 0.734095427 0.001 999
0.999399647 6.36E-04 655 0.49304175 0.001 846
0.999723528 2.92E-04 453 0.526341948 0.001 776
0.999208382 7.97E-04 935 0.689860835 0.001 916
7.29E-04 0.99926752 1664 0.959244533 0.001 1126
8.94E-04 0.999097431 1140 0.507952286 0.001 982
0.999571428 4.49E-04 655 0.549204771 0.001 832
0.999546312 4.57E-04 821 0.495526839 0.001 906
0.999011495 9.88E-04 655 0.487077535 0.001 836
7.44E-04 0.999248803 684 0.80666004 0.001 835
0.999241899 7.54E-04 821 0.487077535 0.001 888
4.41E-04 0.999557943 1710 0.880218688 0.001 1164
6.44E-04 0.999359422 746 0.64612326 0.001 879
0.999084584 9.02E-04 2411 0.487077535 0.001 1361
0.999161359 8.13E-04 1603 0.568091451 0.001 1158
0.999288527 7.18E-04 5553 0.596421471 0.001 2262
0.999245535 7.51E-04 655 0.520874751 0.001 920
0.99933655 6.69E-04 949 0.554671968 0.001 919
0.999672707 3.32E-04 949 0.489562624 0.001 930
0.999124827 8.40E-04 949 0.759940358 0.001 948
0.999418579 5.86E-04 655 0.513916501 0.001 861
0.999556887 4.42E-04 867 0.506461233 0.001 917
0.999461594 5.39E-04 655 0.49055666 0.001 825
0.999055534 9.19E-04 655 0.61332008 0.001 842
7.98E-04 0.999212315 838 0.814612326 0.001 876
0.99928122 7.24E-04 2179 0.631709742 0.001 1296
6.24E-04 0.999366518 872 0.885685885 0.001 901
0.999041541 9.36E-04 1331 0.50944334 0.001 1038
0.999592587 4.05E-04 949 0.529821074 0.001 966
9.64E-04 0.999060263 420 0.845427435 0.001 771
0.999376163 6.29E-04 1039 0.487077535 0.001 941
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140178
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148269
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147296
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171509
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8.30E-08 0.99999992 69456 0.980616302 1.00E-07 27391
0.999999908 9.40E-08 43963 0.952286282 1.00E-07 17831
0.999999933 6.85E-08 57895 0.972166998 1.00E-07 22895
0.999999903 9.66E-08 147859 0.967196819 1.00E-07 56820
0.999999909 9.19E-08 147859 0.977634195 1.00E-07 57323
0.999999918 8.45E-08 30685 0.888667992 1.00E-07 12594
0.999999916 8.33E-08 100417 0.946819085 1.00E-07 38950
0.999999926 7.37E-08 87603 0.984095427 1.00E-07 34551
0.999999902 9.89E-08 157551 0.985586481 1.00E-07 61105
8.67E-08 0.999999914 206478 0.975646123 1.00E-07 79820
0.999999908 9.36E-08 33967 0.973658052 1.00E-07 13941
0.999999914 8.64E-08 130173 0.919980119 1.00E-07 50646
0.999999934 6.64E-08 59761 0.95526839 1.00E-07 23755
7.36E-08 0.999999925 60862 0.912524851 1.00E-07 24338
8.01E-08 0.999999922 80938 0.887673956 1.00E-07 31746
0.999999902 9.83E-08 227827 0.979622266 1.00E-07 87618
0.999999915 8.64E-08 147755 0.934393638 1.00E-07 57173
0.999999909 9.29E-08 107151 0.960238569 1.00E-07 41299
0.999999928 7.06E-08 124675 0.867793241 1.00E-07 47982
0.999999906 9.05E-08 69751 0.798707753 1.00E-07 27316
0.999999927 7.51E-08 53133 0.929423459 1.00E-07 20859
6.32E-08 0.999999936 86842 0.799701789 1.00E-07 33805
0.999999944 5.53E-08 171087 0.916003976 1.00E-07 65682
7.41E-08 0.999999926 95640 0.817097416 1.00E-07 36939
0.999999906 9.33E-08 77441 0.954274354 1.00E-07 30341
0.999999936 6.62E-08 60195 0.899105368 1.00E-07 23884
0.999999909 8.83E-08 103899 0.960238569 1.00E-07 40625
0.999999957 4.29E-08 25613 0.909045726 1.00E-07 10694
0.999999924 7.45E-08 119737 0.934393638 1.00E-07 45841
8.34E-08 0.999999913 38458 0.985586481 1.00E-07 15589
0.999999929 7.39E-08 73915 0.953280318 1.00E-07 28895
0.999999955 4.49E-08 100179 0.882703777 1.00E-07 38830
0.999999912 8.78E-08 87159 0.910536779 1.00E-07 33813
0.999999931 7.19E-08 54187 0.943836978 1.00E-07 21405
0.999999901 9.82E-08 90399 0.976640159 1.00E-07 34986
0.999999905 9.49E-08 77163 0.953777336 1.00E-07 29903
9.85E-08 0.999999901 137118 0.975149105 1.00E-07 -45075
8.03E-08 0.999999915 64356 0.984095427 1.00E-07 24856
0.999999928 7.50E-08 110277 0.959741551 1.00E-07 59343
0.999999914 8.85E-08 37533 0.876242545 1.00E-07 15179
0.999999927 7.09E-08 127813 0.961729622 1.00E-07 49090
0.999999911 9.01E-08 179143 0.961729622 1.00E-07 68756
7.04E-08 0.999999927 27610 0.947316103 1.00E-07 11493
0.99999994 5.96E-08 85537 0.949801193 1.00E-07 33365
0.999999927 7.37E-08 147805 0.963717694 1.00E-07 57069
0.999999914 8.61E-08 103897 0.941848907 1.00E-07 40577
9.12E-08 0.999999907 37606 0.946322068 1.00E-07 15207
0.999999959 3.93E-08 215135 0.930417495 1.00E-07 82648
0.999999939 6.17E-08 70197 0.919483101 1.00E-07 27779
0.999999928 7.12E-08 107595 0.972664016 1.00E-07 41397
0.99999995 5.04E-08 87159 0.967196819 1.00E-07 33960
6.55E-08 0.999999933 105082 0.937872763 1.00E-07 40770
0.999999913 8.49E-08 55625 0.68389662 1.00E-07 21912
0.999999944 5.78E-08 23149 0.822564612 1.00E-07 9609
0.999999912 9.25E-08 63955 0.933399602 1.00E-07 25130
0.999999946 5.27E-08 170051 0.969681909 1.00E-07 65453
0.999999923 7.61E-08 47899 0.964214712 1.00E-07 19048
3427171
本次数据比较多有感兴趣的朋友可以到我的资源里下载