概述
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 | |||
学习率用r表示 | |||
r=0.6 | |||
δ=0.1 | 迭代次数 | ||
minst 0-1 | 1 | 判断是否达到收敛 | |
minst 2-1 | 2 | 判断是否达到收敛 | |
梯度下降 | |||
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
δr | 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
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148269
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147296
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171509
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174402
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7.98E-07 0.999999193 29820 0.969184891 1.00E-06 12090
0.999999376 6.36E-07 25263 0.908548708 1.00E-06 10411
0.999999133 8.96E-07 67167 0.965208748 1.00E-06 26184
0.999999543 4.56E-07 60345 0.85387674 1.00E-06 23518
0.999999099 8.94E-07 145931 0.798210736 1.00E-06 56083
0.999999244 7.41E-07 107875 0.970178926 1.00E-06 41538
0.999999149 8.64E-07 30685 0.943836978 1.00E-06 12406
7.76E-07 0.999999213 25990 0.966699801 1.00E-06 10607
6.41E-07 0.999999357 10602 0.967196819 1.00E-06 4917
0.999999299 7.36E-07 40353 0.872266402 1.00E-06 15974
0.999999081 9.64E-07 23145 0.783797217 1.00E-06 9584
0.999999211 7.68E-07 20217 0.919483101 1.00E-06 8632
2.33E-07 0.999999756 9828 0.955765408 1.00E-06 4604
0.999999081 9.11E-07 20211 0.806163022 1.00E-06 8545
0.99999941 5.82E-07 40353 0.738568588 1.00E-06 16477
0.999999178 8.19E-07 67611 0.973658052 1.00E-06 26358
9.38E-07 0.999999039 37606 0.906063618 1.00E-06 14995
0.999999303 6.87E-07 37407 0.930914513 1.00E-06 15005
0.999999034 9.28E-07 17703 0.547713718 1.00E-06 7561
0.999999244 7.02E-07 5987 0.594930417 1.00E-06 3166
0.99999917 8.00E-07 50205 0.879224652 1.00E-06 19781
0.999999349 6.42E-07 23867 0.981610338 1.00E-06 9840
0.999999149 8.58E-07 15617 0.945328032 1.00E-06 6791
0.999999472 5.13E-07 27907 0.963717694 1.00E-06 11435
8.61E-07 0.999999135 78420 0.970675944 1.00E-06 30273
6.25E-07 0.999999408 29962 0.656560636 1.00E-06 12232
0.99999909 9.15E-07 143923 0.954274354 1.00E-06 54780
0.999999269 7.37E-07 57171 0.946322068 1.00E-06 22328
0.999999703 2.86E-07 45045 0.577037773 1.00E-06 -62942
0.999999081 9.28E-07 60777 0.977137177 1.00E-06 23923
0.999999311 6.99E-07 27183 0.940357853 1.00E-06 11297
0.999999194 7.94E-07 19771 0.737077535 1.00E-06 8386
0.999999531 4.88E-07 50349 0.915506958 1.00E-06 19744
0.999999206 7.74E-07 17703 0.873757455 1.00E-06 7628
6.18E-07 0.999999379 27610 0.932405567 1.00E-06 11221
0.999999212 7.55E-07 5569 0.761431412 1.00E-06 3066
0.999999192 8.21E-07 31143 0.849403579 1.00E-06 12554
0.999999277 7.01E-07 67611 0.985089463 1.00E-06 26305
0.999999221 7.65E-07 81329 0.950795229 1.00E-06 31370
7.31E-07 0.999999262 72910 0.980119284 1.00E-06 28079
0.999999338 6.72E-07 7591 0.907554672 1.00E-06 3800
8.92E-07 0.999999138 99424 0.968687873 1.00E-06 38103
0.999999411 5.87E-07 11151 0.809642147 1.00E-06 5119
0.999999068 9.73E-07 65545 0.964214712 1.00E-06 25467
8.88E-07 0.999999136 39448 0.969681909 1.00E-06 15756
0.999999464 5.43E-07 50205 0.928926441 1.00E-06 19875
5.82E-07 0.999999434 185604 0.969681909 1.00E-06 72589
9.55E-07 0.99999905 27596 0.849403579 1.00E-06 11420
0.999999161 8.18E-07 20793 0.960238569 1.00E-06 8828
0.999999126 8.66E-07 41139 0.821570577 1.00E-06 16559
0.999999228 7.47E-07 77427 0.955765408 1.00E-06 30441
0.999999537 4.76E-07 33141 0.824055666 1.00E-06 13602
0.999999432 5.77E-07 35275 0.938866799 1.00E-06 14371
0.99999927 7.30E-07 27907 0.957753479 1.00E-06 11574
9.05E-07 0.999999117 17614 0.952286282 1.00E-06 7792
0.99999934 6.85E-07 15643 0.817097416 1.00E-06 6944
9.54E-07 0.999999099 8118 0.963717694 1.00E-06 4016
0.999999253 7.63E-07 34283 0.889662028 1.00E-06 13869
9.29E-07 0.999999047 63240 0.977137177 1.00E-06 25117
9.59E-07 0.999999082 39748 0.98111332 1.00E-06 16086
9.16E-07 0.999999047 200846 0.965705765 1.00E-06 77531
0.999999322 7.03E-07 23173 0.977634195 1.00E-06 9831
0.999999087 9.22E-07 50199 0.913021869 1.00E-06 19931
0.999999222 7.76E-07 57171 0.96471173 1.00E-06 22686
9.54E-07 0.999999066 95086 0.944333996 1.00E-06 37191
0.999999551 4.30E-07 37903 0.962723658 1.00E-06 15346
0.999999238 7.29E-07 17683 0.818588469 1.00E-06 7643
0.999999214 7.85E-07 87883 0.968687873 1.00E-06 34319
0.999999171 8.43E-07 19777 0.910039761 1.00E-06 8627
0.999999467 5.56E-07 15983 0.706262425 1.00E-06 7033
0.999999174 8.21E-07 17857 0.970675944 1.00E-06 7872
0.999999067 9.46E-07 7707 0.611332008 1.00E-06 3840
9.81E-07 0.999999037 49922 0.975646123 1.00E-06 19884
0.999999252 7.57E-07 25233 0.96222664 1.00E-06 10627
0.99999915 8.87E-07 60777 0.962723658 1.00E-06 24195
1687743
0.999999944 5.54E-08 70197 0.910536779 1.00E-07 27484
0.99999991 8.96E-08 27857 0.96471173 1.00E-07 11554
0.999999905 9.56E-08 67885 0.970178926 1.00E-07 26803
0.999999916 8.34E-08 73847 0.973161034 1.00E-07 28795
0.99999996 4.12E-08 47619 0.978131213 1.00E-07 18921
0.999999927 7.36E-08 37849 0.929423459 1.00E-07 15274
0.999999903 9.81E-08 47619 0.985089463 1.00E-07 19022
0.999999961 3.98E-08 37851 0.963220676 1.00E-07 15353
0.999999902 9.84E-08 25637 0.749502982 1.00E-07 10544
0.999999921 7.94E-08 60777 0.969681909 1.00E-07 23803
5.68E-08 0.999999946 177678 0.982107356 1.00E-07 67850
8.66E-08 0.999999917 49778 0.955765408 1.00E-07 19945
0.999999932 6.75E-08 126019 0.929423459 1.00E-07 48262
0.999999912 8.56E-08 77887 0.954771372 1.00E-07 30313
0.999999965 3.46E-08 69793 0.919980119 1.00E-07 27394
9.76E-08 0.999999903 125500 0.915506958 1.00E-07 48546
0.999999917 8.23E-08 35073 0.931908549 1.00E-07 14343
0.99999993 7.10E-08 27907 0.967196819 1.00E-07 11487
9.03E-08 0.999999912 227530 0.977137177 1.00E-07 87237
0.999999913 8.44E-08 73847 0.979622266 1.00E-07 28536
0.999999912 9.00E-08 84263 0.919980119 1.00E-07 32709
0.999999902 9.91E-08 267201 0.958747515 1.00E-07 101497
0.999999902 9.61E-08 17631 0.973161034 1.00E-07 7642
0.999999942 5.98E-08 80337 0.908548708 1.00E-07 31072
0.999999934 6.71E-08 107151 0.935884692 1.00E-07 41144
0.999999935 6.39E-08 195769 0.967196819 1.00E-07 74881
7.84E-08 0.999999918 89796 0.98359841 1.00E-07 34566
0.999999905 9.24E-08 153919 0.97415507 1.00E-07 58643
0.999999974 2.67E-08 117147 0.846918489 1.00E-07 45188
2.46E-08 0.999999975 100380 0.826540755 1.00E-07 38789
0.999999951 4.94E-08 37179 0.967196819 1.00E-07 15157
0.999999905 9.88E-08 83905 0.962723658 1.00E-07 32785
0.999999907 9.08E-08 43921 0.968190855 1.00E-07 17475
6.50E-08 0.999999934 58428 0.925447316 1.00E-07 23232
9.07E-08 0.999999905 93142 0.863817097 1.00E-07 36320
0.999999923 7.63E-08 101093 0.947316103 1.00E-07 39184
8.38E-08 0.99999991 108408 0.88667992 1.00E-07 41959
0.999999954 4.58E-08 50349 0.892147117 1.00E-07 20170
0.99999995 4.99E-08 67167 0.930417495 1.00E-07 26336
0.999999951 4.91E-08 57841 0.960238569 1.00E-07 22975
0.999999903 9.94E-08 77833 0.942842942 1.00E-07 30257
0.999999904 9.56E-08 127143 0.929423459 1.00E-07 48973
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
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