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制作一个带一个3*3卷积核的神经网络,测试集是minst的0和1图片集,将28*28的图片缩小成9*9,隐藏层30个节点所以网络的结构是
这个网络分成两个部分左边的是让minst 0向1,0收敛,右边的是让minst 1向 0,1收敛。但是让左右两边的权重实现同步更新,实现权重共享。
w=w,w1=w1,w2=w2
所以上图可以简写成
S(minst0)81-(con3*3)49-30-2-(1,0)
S(minst1)81-(con3*3)49-30-2-(0,1)
w=w,w1=w1,w2=w2
进一步简写成
d2(minst0,1)81-con(3*3)49-30-2-(2*k) ,k∈(0,1)
这个网络的收敛标准是
if (Math.abs(f2[0]-y[0])< δ && Math.abs(f2[1]-y[1])< δ )
本文尝试了δ从0.1到1e-6在内的22个值,测试集是misnt0和1的前4999张图片,
具体进样顺序 | |||
进样顺序 | 迭代次数 | ||
minst 0-1 | 1 | 判断是否达到收敛 | |
minst 1-1 | 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 1-2 | 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 1-3 | 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 1-4 | 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 1-5 | 10 | 判断是否达到收敛 | |
梯度下降 | |||
每5张图测量一次准确率 | |||
…… | |||
minst 0-4999 | 9997 | 判断是否达到收敛 | |
minst 1-4999 | 9998 | 判断是否达到收敛 | |
梯度下降 | |||
测量准确率 | |||
…… | |||
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 1-1 | 10000 | 判断是否达到收敛 | |
…… | |||
达到收敛测量准确率 |
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,测试集中有980个0和1135个1.将整个过程重复199次,取平均值,得到的数据
f2[0] | f2[1] | 迭代次数n | 最大准确率p-max | δ | 耗时ms/次 | 耗时ms/199次 |
0.7814199 | 0.218622 | 2081.13065 | 0.997163121 | 0.1 | 1281.19598 | 254970 |
0.8733397 | 0.126665 | 2850.23618 | 0.998108747 | 0.01 | 1507.32663 | 299964 |
0.8587619 | 0.141238 | 4126.90955 | 0.998108747 | 0.001 | 1877.85427 | 373699 |
0.803976 | 0.196024 | 5887.70854 | 0.99858156 | 1.00E-04 | 2395.20101 | 476650 |
0.8542262 | 0.145774 | 5996.66332 | 0.998108747 | 9.00E-05 | 2425.51759 | 482683 |
0.8743291 | 0.125671 | 6169.33668 | 0.999054374 | 8.00E-05 | 2475.8995 | 492710 |
0.8994586 | 0.100541 | 6184.60804 | 0.999054374 | 7.00E-05 | 2481.05025 | 493736 |
0.8743398 | 0.12566 | 6469.72864 | 0.999054374 | 6.00E-05 | 2564.65327 | 510374 |
0.9145433 | 0.085457 | 6686.59297 | 0.999054374 | 5.00E-05 | 2630.86935 | 523546 |
0.8994744 | 0.100526 | 7160.33668 | 1 | 4.00E-05 | 2760.78895 | 549400 |
0.8894298 | 0.11057 | 7711.47236 | 0.99858156 | 3.00E-05 | 2918.30653 | 580745 |
0.8693348 | 0.130665 | 8744.00503 | 0.999054374 | 2.00E-05 | 3217.62312 | 640314 |
0.8542662 | 0.145734 | 9885.65829 | 0.999527187 | 1.00E-05 | 3550.23618 | 706499 |
0.8442164 | 0.155784 | 9949.09548 | 0.999054374 | 9.00E-06 | 3568.73367 | 710183 |
0.7989913 | 0.201009 | 10597.7839 | 0.999054374 | 8.00E-06 | 3759.81407 | 748203 |
0.8241173 | 0.175883 | 10781.608 | 0.999054374 | 7.00E-06 | 4745.38191 | 944331 |
0.7939673 | 0.206033 | 11409.8694 | 0.99858156 | 6.00E-06 | 5653.84925 | 1125133 |
0.819093 | 0.180907 | 11777.7236 | 0.999054374 | 5.00E-06 | 5779.13568 | 1150051 |
0.8391939 | 0.160806 | 12539.7337 | 0.999527187 | 4.00E-06 | 7554.22613 | 1503296 |
0.7738681 | 0.226132 | 13767.3769 | 0.999527187 | 3.00E-06 | 7585.08543 | 1509433 |
0.8040192 | 0.195981 | 14645.2965 | 0.999527187 | 2.00E-06 | 6719.0402 | 1337089 |
0.7939694 | 0.206031 | 18080.9347 | 0.999054374 | 1.00E-06 | 8229.86935 | 1637744 |
拟合迭代次数曲线n(δ)
δ | 1.00E-06, 2.00E-06, 3.00E-06, 4.00E-06, 5.00E-06, 6.00E-06, 7.00E-06, 8.00E-06, 9.00E-06, 1.00E-05, 2.00E-05, 3.00E-05, 4.00E-05, 5.00E-05, 6.00E-05, 7.00E-05, 8.00E-05, 9.00E-05, 1.00E-04 |
n | 18080.93467, 14645.29648, 13767.37688, 12539.73367, 11777.72362, 11409.86935, 10781.60804, 10597.78392, 9949.095477, 9885.658291, 8744.005025, 7711.472362, 7160.336683, 6686.592965, 6469.728643, 6184.60804, 6169.336683, 5996.663317, 5887.708543 |
拟合方程式:y=ax^b | |
a = 619.836438471378 | |
b = -0.242000298691936 | |
相关系数 R2:0.996504987605224 |
http://www.qinms.com/webapp/curvefit/cf.aspx
与实测值比较
δ | 实测迭代次数n | 计算值 | 实测值/计算值 |
0.1 | 2081.130653 | 1082.125 | 1.9231889 |
0.01 | 2850.236181 | 1889.199 | 1.508700894 |
0.001 | 4126.909548 | 3298.208 | 1.25125824 |
1.00E-04 | 5887.708543 | 5758.088 | 1.022511038 |
9.00E-05 | 5996.663317 | 5906.791 | 1.015215036 |
8.00E-05 | 6169.336683 | 6077.578 | 1.015097882 |
7.00E-05 | 6184.60804 | 6277.181 | 0.985252537 |
6.00E-05 | 6469.728643 | 6515.77 | 0.992933821 |
5.00E-05 | 6686.592965 | 6809.695 | 0.981922564 |
4.00E-05 | 7160.336683 | 7187.534 | 0.996216073 |
3.00E-05 | 7711.472362 | 7705.753 | 1.000742158 |
2.00E-05 | 8744.005025 | 8500.202 | 1.028682023 |
1.00E-05 | 9885.658291 | 10052.6 | 0.983392739 |
9.00E-06 | 9949.095477 | 10312.21 | 0.964787516 |
8.00E-06 | 10597.78392 | 10610.38 | 0.998813057 |
7.00E-06 | 10781.60804 | 10958.85 | 0.983826738 |
6.00E-06 | 11409.86935 | 11375.38 | 1.003031589 |
5.00E-06 | 11777.72362 | 11888.52 | 0.990680039 |
4.00E-06 | 12539.73367 | 12548.16 | 0.999328135 |
3.00E-06 | 13767.37688 | 13452.88 | 1.023377329 |
2.00E-06 | 14645.29648 | 14839.85 | 0.986889711 |
1.00E-06 | 18080.93467 | 17550.07 | 1.030248477 |
所以d2(minst0,1)81-con(3*3)49-30-2-(2*k) ,k∈(0,1)的特征频率曲线可以近似的表示为
迭代次数曲线n(δ)
在《神经网络收敛标准与准确率之间的数学关系》计算了
d2(minst0,2)81-con(3*3)49-30-2-(2*k) ,k∈(0,1)的数据,用本文的方法
可以同样的拟合到n(δ)曲线
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, |
n | 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 |
δ | 实测迭代次数n | 计算值 | 实测值/计算值 |
0.1 | 2785.725 | 154.22 | 18.06331651 |
1.00E-02 | 3620.905 | 535.618 | 6.760237761 |
1.00E-03 | 4846.435 | 1860.243 | 2.605270502 |
1.00E-04 | 7709.22 | 6460.766 | 1.193236306 |
0.00009 | 7951.16 | 6839.521 | 1.16253163 |
0.00008 | 8182.51 | 7289.28 | 1.122540254 |
0.00007 | 8193.28 | 7835.049 | 1.045721638 |
0.00006 | 8780.59 | 8516.101 | 1.03105754 |
0.00005 | 9227.095 | 9398.428 | 0.981769995 |
0.00004 | 9473.415 | 10603.66 | 0.893409745 |
0.00003 | 10478 | 12388.31 | 0.845797391 |
0.00002 | 12060.835 | 15425.07 | 0.781898252 |
1.00E-05 | 19757.36 | 22438.74 | 0.880502356 |
0.000009 | 22245.54 | 23754.18 | 0.936489317 |
0.000008 | 22214.925 | 25316.23 | 0.877497363 |
0.000007 | 28045.605 | 27211.73 | 1.03064405 |
0.000006 | 28410.335 | 29577.07 | 0.960552592 |
0.000005 | 33681.76 | 32641.47 | 1.031870341 |
0.000004 | 37281.575 | 36827.33 | 1.012334327 |
0.000003 | 45173.585 | 43025.55 | 1.04992456 |
0.000002 | 60366.615 | 53572.45 | 1.126821901 |
1.00E-06 | 90392.445 | 77931.46 | 1.159896753 |
9E-07 | 99247.645 | 82500.11 | 1.203000132 |
8E-07 | 95016.955 | 87925.22 | 1.080656493 |
7E-07 | 113411.54 | 94508.42 | 1.200015139 |
6E-07 | 116304.29 | 102723.5 | 1.132207735 |
5E-07 | 129507.275 | 113366.3 | 1.142378729 |
4E-07 | 135768.05 | 127904.2 | 1.061482607 |
3E-07 | 149701.355 | 149431.1 | 1.001808836 |
2E-07 | 155856.775 | 186061.3 | 0.837663768 |
1.00E-07 | 207402.66 | 270662 | 0.76627933 |
1.00E-08 | 369805.81 | 940029.8 | 0.393397965 |
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
f2[0] f2[1] 迭代次数n 最大准确率p-max δ 耗时ms/次
0.901909 0.094579 2623 0.944208 0.1 2771
0.902452 0.096982 1691 0.986761 0.1 1301
0.903692 0.092707 1353 0.935697 0.1 1145
0.933332 0.070365 2685 0.911111 0.1 1468
0.932102 0.069651 1591 0.995272 0.1 1171
0.082392 0.918035 2034 0.993381 0.1 1272
0.919535 0.081082 2263 0.977778 0.1 1316
0.096707 0.904629 2066 0.956974 0.1 1247
0.926324 0.077409 2011 0.96643 0.1 1258
0.921815 0.076874 2635 0.975887 0.1 1494
0.928219 0.075844 1791 0.995745 0.1 1208
0.903384 0.097846 3633 0.984397 0.1 1779
0.912376 0.086648 1791 0.978723 0.1 1192
0.904615 0.095433 1517 0.970213 0.1 1098
0.910305 0.091183 1151 0.98156 0.1 1022
0.90898 0.089259 2635 0.972104 0.1 1411
0.921585 0.082182 2961 0.970686 0.1 1515
0.91115 0.092115 1663 0.941844 0.1 1149
0.904652 0.095851 2169 0.98156 0.1 1279
0.911692 0.087674 1529 0.941844 0.1 1111
0.900635 0.09794 2137 0.982033 0.1 1289
0.909223 0.087863 1837 0.963121 0.1 1188
0.096397 0.904704 2352 0.976832 0.1 1331
0.912611 0.089346 1275 0.993381 0.1 1028
0.919379 0.083862 1353 0.950827 0.1 1066
0.097821 0.901061 1492 0.996217 0.1 1082
0.901862 0.097796 1353 0.914894 0.1 1050
0.933322 0.067604 1353 0.960284 0.1 1040
0.906359 0.0887 1839 0.988652 0.1 1218
0.923679 0.077108 2137 0.928605 0.1 1272
0.908456 0.09597 2411 0.970213 0.1 1359
0.926782 0.072535 1529 0.947518 0.1 1109
0.930866 0.071064 1529 0.903073 0.1 1098
0.906591 0.093533 1353 0.945154 0.1 1052
0.098099 0.903661 2034 0.993381 0.1 1257
0.906606 0.090892 2065 0.991962 0.1 1246
0.906258 0.093159 2401 0.986288 0.1 1363
0.90201 0.098307 1787 0.991489 0.1 1167
0.921274 0.080214 1885 0.989598 0.1 1199
0.90409 0.094316 2195 0.947991 0.1 1313
0.08865 0.91299 2200 0.988652 0.1 1287
0.904668 0.096382 2433 0.692199 0.1 1368
0.926968 0.075046 1181 0.992908 0.1 1001
0.912832 0.088924 1507 0.990071 0.1 1094
0.911962 0.094832 1685 0.996217 0.1 1179
0.910534 0.088941 2037 0.993381 0.1 1239
0.915459 0.084935 1837 0.959811 0.1 1205
0.094748 0.907092 1748 0.992908 0.1 1192
0.916496 0.085786 3139 0.841135 0.1 1588
0.908356 0.090996 1981 0.988652 0.1 1235
0.912766 0.088389 2699 0.976832 0.1 1440
0.097333 0.902375 1576 0.974941 0.1 1118
0.906119 0.091208 2623 0.986288 0.1 1431
0.913814 0.083497 1353 0.956501 0.1 1048
0.901039 0.097512 2275 0.891726 0.1 1321
0.922374 0.078138 3181 0.955083 0.1 1580
0.918561 0.086148 2961 0.989125 0.1 1511
0.9108 0.089535 2879 0.947991 0.1 1489
0.09868 0.902377 3036 0.995745 0.1 1535
0.070987 0.929697 2886 0.978251 0.1 1508
0.91014 0.089267 2273 0.98818 0.1 1379
0.906139 0.091849 2623 0.972104 0.1 1431
0.91415 0.085417 2011 0.941371 0.1 1254
0.936036 0.062225 2119 0.979196 0.1 1273
0.920447 0.079878 3545 0.723877 0.1 1697
0.920989 0.07879 1297 0.98487 0.1 1039
0.92304 0.07526 1275 0.986761 0.1 1030
0.906044 0.096978 2741 0.852482 0.1 1445
0.087458 0.911398 2886 0.992908 0.1 1518
0.913959 0.087725 3733 0.87896 0.1 1746
0.906147 0.092283 2037 0.975414 0.1 1250
0.09737 0.900312 1492 0.990071 0.1 1092
0.916339 0.08712 1353 0.929551 0.1 1050
0.094453 0.905978 2066 0.975414 0.1 1261
0.91549 0.08811 2623 0.946099 0.1 1424
0.902189 0.095745 2011 0.994326 0.1 1242
0.922538 0.077319 3585 0.891726 0.1 1688
0.915496 0.084972 2281 0.968794 0.1 1337
0.918776 0.082689 2037 0.985816 0.1 1247
0.907195 0.090804 1353 0.904965 0.1 1057
0.917349 0.087013 1297 0.991962 0.1 1042
0.902716 0.096263 1519 0.986761 0.1 1093
0.900913 0.096878 3905 0.968794 0.1 1832
0.926933 0.076426 1297 0.993853 0.1 1086
0.094858 0.901795 2060 0.955083 0.1 1255
0.919575 0.078784 2823 0.980615 0.1 1497
0.90843 0.090789 2411 0.945626 0.1 1357
0.906813 0.093643 1519 0.991962 0.1 1112
0.907608 0.091837 1353 0.959338 0.1 1062
0.084091 0.917777 1456 0.986761 0.1 1109
0.905097 0.093284 2357 0.991489 0.1 1335
0.929178 0.074454 3181 0.803783 0.1 1575
0.904824 0.092016 2263 0.973522 0.1 1311
0.910088 0.090933 1663 0.989125 0.1 1140
0.098363 0.900825 1576 0.974468 0.1 1129
0.908968 0.090907 1181 0.991017 0.1 995
0.908266 0.091867 2191 0.962648 0.1 1287
0.931624 0.066225 2011 0.992435 0.1 1241
0.919288 0.082889 1297 0.99669 0.1 1033
0.086942 0.914041 2174 0.991962 0.1 1289
0.917313 0.084702 2557 0.93617 0.1 1383
0.902215 0.098574 1297 0.992908 0.1 1038
0.925603 0.074606 1297 0.983924 0.1 1035
0.906463 0.094615 2623 0.951773 0.1 1415
0.92445 0.075964 2191 0.964066 0.1 1308
0.916133 0.082373 2089 0.973995 0.1 1260
0.076011 0.92327 1628 0.993381 0.1 1140
0.901788 0.097565 1603 0.931915 0.1 1121
0.095273 0.90517 1308 0.972104 0.1 1055
0.901018 0.096879 1837 0.925296 0.1 1240
0.928987 0.072843 3873 0.96643 0.1 1837
0.091775 0.906264 2280 0.977305 0.1 1335
0.915486 0.081361 2213 0.994326 0.1 1320
0.919161 0.080232 2685 0.953191 0.1 1444
0.907403 0.089361 2191 0.969267 0.1 1306
0.092053 0.90923 2932 0.976359 0.1 1535
0.920307 0.082009 2411 0.977305 0.1 1363
0.900892 0.094514 2263 0.979196 0.1 1325
0.911198 0.088894 1865 0.980615 0.1 1220
0.916402 0.085256 2077 0.978723 0.1 1272
0.91129 0.088416 1155 0.899291 0.1 1005
0.917099 0.082061 1981 0.985816 0.1 1247
0.901542 0.096567 2401 0.986761 0.1 1356
0.914612 0.08751 3905 0.987234 0.1 1808
0.097987 0.900389 1250 0.985343 0.1 1048
0.933606 0.067634 2011 0.972104 0.1 1244
0.923168 0.075106 1837 0.950827 0.1 1206
0.928061 0.067977 1297 0.990544 0.1 1043
0.916319 0.081902 1885 0.997163 0.1 1213
0.905775 0.09555 1691 0.991962 0.1 1163
0.907497 0.09484 2037 0.988652 0.1 1250
0.099344 0.901167 1772 0.990071 0.1 1197
0.9109 0.093385 1387 0.994326 0.1 1064
0.902227 0.099703 1791 0.983452 0.1 1191
0.069445 0.927823 2066 0.981087 0.1 1273
0.909569 0.088812 2557 0.892671 0.1 1419
0.924032 0.07248 1531 0.958865 0.1 1114
0.903008 0.095758 2347 0.991017 0.1 1344
0.91155 0.087807 3067 0.98487 0.1 1576
0.923577 0.073255 2733 0.805201 0.1 1462
0.068574 0.931225 3658 0.975887 0.1 1744
0.919974 0.083332 1917 0.992435 0.1 1232
0.90188 0.0998 1997 0.995745 0.1 1257
0.083579 0.913585 2034 0.995272 0.1 1264
0.093972 0.906858 1492 0.997163 0.1 1098
0.92459 0.079542 1227 0.994326 0.1 1026
0.915577 0.082678 2935 0.871395 0.1 1525
0.910586 0.087674 1181 0.991489 0.1 1026
0.919125 0.082527 2195 0.929551 0.1 1303
0.903847 0.094126 1529 0.915366 0.1 1119
0.92322 0.074983 2613 0.993381 0.1 1425
0.941518 0.058367 3139 0.954137 0.1 1585
0.920318 0.080293 1865 0.956501 0.1 1223
0.91263 0.088375 1529 0.950827 0.1 1110
0.911818 0.0856 1917 0.992435 0.1 1233
0.097049 0.903228 2604 0.987234 0.1 1424
0.91053 0.087642 1275 0.987707 0.1 1045
0.90526 0.096702 2169 0.994799 0.1 1301
0.903749 0.099113 2557 0.973995 0.1 1431
0.91765 0.080636 1529 0.963593 0.1 1121
0.096961 0.902305 1492 0.993381 0.1 1096
0.909187 0.089283 2169 0.986288 0.1 1316
0.912999 0.089431 1517 0.96974 0.1 1110
0.908867 0.090959 1529 0.976359 0.1 1109
0.906807 0.092587 1043 0.980142 0.1 979
0.915645 0.080614 2037 0.930496 0.1 1259
0.905198 0.094158 1791 0.98818 0.1 1206
0.922368 0.078155 2635 0.975887 0.1 1444
0.068538 0.933348 1108 0.992435 0.1 987
0.903909 0.093829 2227 0.978723 0.1 1338
0.924046 0.077944 1691 0.983924 0.1 1160
0.902165 0.0975 2873 0.973995 0.1 1513
0.923848 0.073623 3139 0.813239 0.1 1591
0.939419 0.060476 2191 0.913475 0.1 1304
0.918324 0.081017 1917 0.995745 0.1 1224
0.90609 0.093982 2241 0.995745 0.1 1323
0.92766 0.074362 2011 0.886525 0.1 1254
0.90469 0.091482 1663 0.976359 0.1 1150
0.940047 0.061261 2961 0.935225 0.1 1531
0.914761 0.082157 2623 0.978251 0.1 1440
0.900151 0.098647 2025 0.992908 0.1 1261
0.072283 0.930479 1628 0.990544 0.1 1147
0.9019 0.099523 1155 0.982506 0.1 1001
0.904121 0.094851 2807 0.984397 0.1 1497
0.94879 0.051735 2037 0.953191 0.1 1263
0.901814 0.099132 1973 0.943735 0.1 1249
0.911184 0.089576 2961 0.930496 0.1 1558
0.911108 0.089057 1591 0.960757 0.1 1136
0.904374 0.098008 1701 0.883688 0.1 1163
0.912946 0.085634 1353 0.952719 0.1 1079
0.08779 0.909275 1732 0.989598 0.1 1166
0.929976 0.070056 3241 0.840662 0.1 1622
0.944245 0.057899 1837 0.917258 0.1 1217
0.90462 0.096201 3879 0.980615 0.1 1817
0.920969 0.078625 2401 0.989598 0.1 1361
0.912293 0.086982 1591 0.915839 0.1 1143
0.908631 0.08885 1297 0.986761 0.1 1044
0.904218 0.096723 2003 0.994799 0.1 1255
0.904629 0.094391 2591 0.995745 0.1 1435
254970
0.991259 0.008523 4303 0.997163 0.01 1958
0.992982 0.006914 1529 0.978251 0.01 1108
0.990021 0.009933 2263 0.775887 0.01 1345
0.993982 0.006189 4271 0.958865 0.01 1932
0.992854 0.007279 3913 0.753191 0.01 1828
0.993039 0.007026 1529 0.980615 0.01 1134
0.993181 0.0074 2613 0.995272 0.01 1432
0.009763 0.990374 2034 0.991962 0.01 1264
0.995346 0.00497 3545 0.98156 0.01 1721
0.990206 0.009696 3181 0.994326 0.01 1608
0.992048 0.007724 3181 0.994799 0.01 1589
0.009217 0.990755 2060 0.986288 0.01 1291
0.004339 0.995592 5676 0.988652 0.01 2496
0.009328 0.990699 4874 0.936643 0.01 2103
0.990612 0.009251 2411 0.970686 0.01 1392
0.990811 0.00934 4207 0.994799 0.01 1894
0.00427 0.995751 3658 0.940426 0.01 1757
0.990912 0.008785 2169 0.993853 0.01 1303
0.99527 0.004795 2263 0.976359 0.01 1337
0.991033 0.009056 2037 0.993381 0.01 1262
0.995558 0.004481 3897 0.96643 0.01 1802
0.992327 0.00827 3479 0.9026 0.01 1687
0.990546 0.009268 2893 0.895981 0.01 1508
0.991994 0.007912 3343 0.992908 0.01 1641
0.990431 0.009809 3555 0.9513 0.01 1726
0.991884 0.007959 2893 0.995272 0.01 1523
0.991659 0.008545 3181 0.998109 0.01 1594
0.99014 0.009914 3019 0.982979 0.01 1592
0.992754 0.007461 2787 0.945626 0.01 1554
0.995351 0.00488 2011 0.971631 0.01 1248
0.990286 0.009552 4347 0.983452 0.01 1939
0.990086 0.009672 2263 0.913948 0.01 1309
0.992878 0.006966 3905 0.930496 0.01 1836
0.99029 0.009732 3533 0.992435 0.01 1686
0.991689 0.008568 2635 0.959338 0.01 1433
0.007178 0.992794 2200 0.991962 0.01 1296
0.990707 0.009181 2879 0.995272 0.01 1495
0.992145 0.008102 2635 0.987707 0.01 1427
0.990621 0.009435 4505 0.853428 0.01 2068
0.991988 0.007998 3959 0.78156 0.01 1826
0.990333 0.009631 3181 0.995272 0.01 1596
0.990085 0.009685 2089 0.995272 0.01 1276
0.99031 0.009804 2169 0.993853 0.01 1306
0.993158 0.006517 2037 0.979196 0.01 1257
0.990881 0.008698 2611 0.993381 0.01 1428
0.992415 0.007516 3055 0.980142 0.01 1561
0.009448 0.990079 2174 0.993381 0.01 1300
0.990713 0.009073 2191 0.872813 0.01 1305
0.993864 0.00643 2191 0.970686 0.01 1316
0.991935 0.007785 2263 0.931442 0.01 1325
0.991291 0.008848 2635 0.943735 0.01 1428
0.991758 0.008257 2557 0.906383 0.01 1449
0.990678 0.009031 2011 0.995272 0.01 1252
0.991657 0.00836 3913 0.676596 0.01 1793
0.009725 0.990198 1946 0.993381 0.01 1235
0.991988 0.007925 2077 0.989125 0.01 1290
0.99039 0.009484 2615 0.995272 0.01 1427
0.008233 0.991952 2932 0.926714 0.01 1512
0.991435 0.008715 2191 0.945154 0.01 1310
0.990007 0.009837 2961 0.85721 0.01 1553
0.009566 0.990713 3814 0.989125 0.01 1781
0.991908 0.008127 1701 0.98156 0.01 1165
0.992017 0.007887 2191 0.981087 0.01 1311
0.991125 0.008875 5775 0.875177 0.01 2358
0.991031 0.008942 2191 0.846336 0.01 1322
0.991856 0.00787 2037 0.986288 0.01 1274
0.992169 0.007574 2191 0.973995 0.01 1314
0.990083 0.009771 2191 0.990071 0.01 1314
0.993051 0.007081 2263 0.968794 0.01 1323
0.992074 0.007967 4517 0.97305 0.01 1979
0.990873 0.009222 2077 0.986288 0.01 1281
0.990499 0.009735 1691 0.995745 0.01 1169
0.99015 0.009922 2089 0.995272 0.01 1289
0.993849 0.006416 2879 0.839716 0.01 1504
0.990417 0.009654 4037 0.955083 0.01 1839
0.994294 0.005665 3343 0.886998 0.01 1657
0.990253 0.009981 2011 0.970213 0.01 1251
0.994436 0.005621 3181 0.989598 0.01 1594
0.995314 0.004578 2191 0.986288 0.01 1313
0.009144 0.990969 2066 0.982979 0.01 1282
0.991663 0.008549 2787 0.811348 0.01 1491
0.993675 0.006202 2635 0.990544 0.01 1437
0.99392 0.00599 1701 0.977778 0.01 1167
0.991135 0.008962 3181 0.978723 0.01 1586
0.990284 0.009693 2635 0.983452 0.01 1443
0.990766 0.009668 2879 0.932388 0.01 1520
0.996917 0.002973 2263 0.859574 0.01 1322
0.994874 0.005072 3181 0.964539 0.01 1601
0.993668 0.006434 1691 0.966903 0.01 1182
0.991176 0.00924 2191 0.95461 0.01 1305
0.993135 0.006835 4551 0.586761 0.01 2034
0.992183 0.007647 2635 0.995272 0.01 1435
0.993052 0.007159 3931 0.7513 0.01 1807
0.990172 0.009665 2433 0.754137 0.01 1395
0.991317 0.00847 2711 0.996217 0.01 1452
0.992444 0.007431 2433 0.774468 0.01 1379
0.990315 0.00949 2741 0.939953 0.01 1463
0.990066 0.009724 2263 0.961702 0.01 1329
0.994184 0.005641 2263 0.724823 0.01 1325
0.991089 0.008829 2401 0.996217 0.01 1368
0.992795 0.007195 2263 0.921986 0.01 1321
0.009938 0.990522 2140 0.978723 0.01 1294
0.992054 0.008091 3633 0.993381 0.01 1726
0.009553 0.990054 3664 0.967376 0.01 1743
0.99061 0.009289 2613 0.996217 0.01 1431
0.990189 0.009475 4677 0.867612 0.01 2035
0.991606 0.008309 2961 0.993381 0.01 1530
0.991059 0.008963 2037 0.991017 0.01 1278
0.99192 0.008272 3181 0.996217 0.01 1607
0.991554 0.008516 2893 0.90922 0.01 1521
0.995545 0.004779 2263 0.870922 0.01 1347
0.991867 0.007699 2037 0.982033 0.01 1262
0.99152 0.008077 2191 0.934279 0.01 1302
0.991418 0.008737 1791 0.993853 0.01 1195
0.99244 0.007612 2635 0.978723 0.01 1438
0.009515 0.990355 3388 0.979196 0.01 1668
0.008549 0.991468 2932 0.95461 0.01 1519
0.996548 0.003391 3181 0.979196 0.01 1592
0.009632 0.99053 2604 0.986761 0.01 1444
0.991955 0.007989 2685 0.965485 0.01 1468
0.991197 0.008726 2531 0.993381 0.01 1405
0.99163 0.008375 1663 0.995272 0.01 1167
0.990328 0.009583 2879 0.944208 0.01 1501
0.007772 0.992134 2724 0.990071 0.01 1459
0.991222 0.008862 4551 0.937589 0.01 1990
0.99155 0.008228 2961 0.994326 0.01 1536
0.990493 0.009507 1885 0.996217 0.01 1269
0.006341 0.993461 2620 0.990544 0.01 1460
0.991029 0.008953 2613 0.977305 0.01 1414
0.990219 0.009801 3545 0.990544 0.01 1722
0.991429 0.008851 2613 0.981087 0.01 1420
0.991286 0.008972 2613 0.995272 0.01 1432
0.99275 0.007258 1791 0.993381 0.01 1183
0.993959 0.006153 3181 0.989125 0.01 1590
0.991203 0.00868 2281 0.965012 0.01 1339
0.990693 0.009401 2191 0.983924 0.01 1300
0.991827 0.008393 3417 0.991489 0.01 1671
0.992682 0.007345 2433 0.812766 0.01 1382
0.991612 0.008325 2635 0.990544 0.01 1433
0.007979 0.991858 2820 0.981087 0.01 1486
0.991534 0.008449 2433 0.476123 0.01 1388
0.991286 0.008849 2685 0.973522 0.01 1462
0.995978 0.004068 3537 0.474704 0.01 1715
0.991501 0.008422 2263 0.948463 0.01 1323
0.007375 0.992704 2462 0.991962 0.01 1385
0.993634 0.006388 3969 0.46383 0.01 1823
0.991985 0.007683 2013 0.955556 0.01 1261
0.991374 0.00838 2077 0.986761 0.01 1269
0.990596 0.009546 2893 0.98487 0.01 1511
0.990561 0.009145 5055 0.919622 0.01 2140
0.993241 0.006979 4677 0.87896 0.01 2055
0.007194 0.992921 3272 0.994326 0.01 1634
0.990401 0.009314 2037 0.996217 0.01 1259
0.009599 0.990289 2906 0.984397 0.01 1523
0.990525 0.009478 2635 0.988652 0.01 1436
0.993283 0.006572 3181 0.991962 0.01 1609
0.99275 0.007281 3181 0.994326 0.01 1646
0.990372 0.009602 3959 0.972104 0.01 1834
0.995033 0.004943 2263 0.820804 0.01 1327
0.991021 0.009095 2663 0.948936 0.01 1454
0.993259 0.006731 2011 0.994799 0.01 1272
0.995928 0.004047 3913 0.505437 0.01 1812
0.99 0.009969 2623 0.993381 0.01 1450
0.994913 0.00528 1529 0.920567 0.01 1126
0.994336 0.005664 3633 0.994799 0.01 1731
0.990824 0.009491 3733 0.961702 0.01 1757
0.991652 0.008363 2879 0.997163 0.01 1527
0.995485 0.004529 3181 0.879433 0.01 1605
0.992809 0.006789 1353 0.937116 0.01 1083
0.991049 0.009181 3325 0.98818 0.01 1646
0.993782 0.006397 3537 0.711584 0.01 1706
0.991312 0.009063 2635 0.987707 0.01 1445
0.991293 0.008514 2685 0.984397 0.01 1444
0.995545 0.004489 2037 0.980615 0.01 1268
0.990212 0.009416 3747 0.961702 0.01 1804
0.99126 0.008648 2961 0.990071 0.01 1542
0.990298 0.009751 1701 0.972577 0.01 1166
0.990332 0.009958 2879 0.892671 0.01 1518
0.990309 0.009844 3325 0.99669 0.01 1640
0.991294 0.008656 2685 0.96974 0.01 1456
0.009495 0.990687 3270 0.987234 0.01 1631
0.991382 0.00868 2433 0.872813 0.01 1374
0.993945 0.00617 3181 0.990544 0.01 1605
0.991749 0.008098 4843 0.812766 0.01 2095
0.993224 0.00686 3545 0.8 0.01 1716
0.993902 0.00599 2263 0.980615 0.01 1397
0.992093 0.007859 4889 0.913948 0.01 2166
0.991306 0.008991 2975 0.992908 0.01 1553
0.990166 0.009822 2077 0.988652 0.01 1281
0.990237 0.009692 2879 0.964539 0.01 1522
0.99353 0.006759 3545 0.83357 0.01 1704
0.990149 0.009932 2807 0.991962 0.01 1493
0.991276 0.00863 1701 0.922459 0.01 1163
0.990436 0.009824 3613 0.914421 0.01 1766
0.992443 0.007514 2961 0.996217 0.01 1563
0.008558 0.991255 3612 0.977778 0.01 1733
0.990984 0.009178 2507 0.991017 0.01 1412
0.992918 0.007176 2037 0.982033 0.01 1259
0.992761 0.007478 2433 0.647754 0.01 1369
299964
0.999522 4.72E-04 4341 0.974468 0.001 1940
0.999159 8.39E-04 4303 0.968322 0.001 1923
0.999273 7.31E-04 3897 0.996217 0.001 1803
0.999243 7.29E-04 4341 0.962175 0.001 1927
0.999075 9.31E-04 3545 0.990071 0.001 1699
0.999433 5.77E-04 3969 0.683215 0.001 1825
0.999055 9.06E-04 4211 0.991489 0.001 1899
0.999134 8.80E-04 3325 0.992908 0.001 1641
0.999053 9.19E-04 4303 0.991017 0.001 1925
0.999376 6.12E-04 3897 0.983452 0.001 1800
0.999091 8.96E-04 3325 0.997163 0.001 1651
0.999196 7.70E-04 4949 0.576359 0.001 2104
0.999057 9.46E-04 3633 0.99669 0.001 1727
0.999068 9.38E-04 3959 0.964066 0.001 1827
0.999171 8.35E-04 3835 0.977778 0.001 1787
0.999092 9.19E-04 4949 0.643499 0.001 2110
0.999414 5.90E-04 3931 0.881324 0.001 1808
0.999386 6.18E-04 3897 0.973995 0.001 1805
0.99915 8.49E-04 3969 0.648227 0.001 1828
0.999109 8.57E-04 3897 0.989125 0.001 1802
0.999093 9.30E-04 6121 0.765012 0.001 2481
0.999627 3.73E-04 4677 0.832151 0.001 2067
0.999159 8.37E-04 3883 0.990071 0.001 1779
9.46E-04 0.999076 3264 0.991962 0.001 1640
0.99927 7.39E-04 3969 0.693144 0.001 1815
0.999131 8.71E-04 3533 0.996217 0.001 1701
0.999433 5.59E-04 4951 0.995745 0.001 2103
0.999033 9.76E-04 4341 0.991017 0.001 1931
0.999067 9.56E-04 3019 0.995272 0.001 1555
0.999051 9.42E-04 4889 0.938534 0.001 2092
9.57E-04 0.999045 4476 0.986288 0.001 1969
0.999399 5.89E-04 3897 0.982506 0.001 1801
8.50E-04 0.999157 5006 0.986288 0.001 2123
0.999333 6.64E-04 4303 0.996217 0.001 1937
7.68E-04 0.999225 5112 0.87896 0.001 2193
0.999478 5.08E-04 6357 0.552719 0.001 2532
0.999391 6.15E-04 6121 0.467139 0.001 2467
0.999195 7.95E-04 3969 0.548936 0.001 1839
0.999345 6.61E-04 3533 0.977778 0.001 1734
0.99928 7.28E-04 4505 0.978251 0.001 2018
0.999326 7.00E-04 3897 0.989598 0.001 1815
0.999179 8.23E-04 5271 0.67565 0.001 2226
0.999142 8.41E-04 3919 0.956974 0.001 1809
0.999137 8.62E-04 3325 0.993853 0.001 1640
0.999142 8.80E-04 4843 0.831678 0.001 2089
0.999161 8.33E-04 3897 0.957447 0.001 1797
0.999084 8.95E-04 3633 0.988652 0.001 1733
0.999159 8.32E-04 4889 0.960757 0.001 2107
0.999369 6.31E-04 3325 0.995272 0.001 1647
0.999556 4.50E-04 5467 0.936643 0.001 2267
0.999034 9.58E-04 3931 0.986288 0.001 1836
0.999392 6.16E-04 4271 0.928605 0.001 1924
7.16E-04 0.999269 2932 0.91773 0.001 1517
0.999053 9.53E-04 3561 0.882742 0.001 1712
0.999022 9.75E-04 2685 0.995745 0.001 1457
0.999089 9.31E-04 4319 0.991962 0.001 1939
0.999065 9.20E-04 3615 0.991017 0.001 1718
5.38E-04 0.999456 3954 0.963593 0.001 1832
0.999286 7.33E-04 3325 0.998109 0.001 1637
2.73E-04 0.999724 5676 0.958865 0.001 2332
0.999088 9.16E-04 3181 0.997636 0.001 1632
0.999166 8.27E-04 4657 0.907801 0.001 2058
0.999406 6.01E-04 3325 0.995745 0.001 1643
0.999685 3.14E-04 3897 0.965012 0.001 1802
0.999402 5.89E-04 4677 0.900709 0.001 2062
0.999132 8.81E-04 4677 0.769267 0.001 2046
0.99935 6.52E-04 5271 0.882742 0.001 2204
0.999092 8.93E-04 3019 0.993381 0.001 1579
0.999196 8.47E-04 3747 0.748936 0.001 1756
0.999233 7.80E-04 4299 0.877069 0.001 1922
0.999336 6.65E-04 3897 0.992435 0.001 1805
7.05E-04 0.999297 3876 0.982506 0.001 1811
0.999157 8.35E-04 3181 0.986761 0.001 1596
0.999236 7.57E-04 3897 0.981087 0.001 1835
0.999149 8.71E-04 4299 0.653428 0.001 1955
0.999332 6.63E-04 5467 0.550827 0.001 2253
0.999061 9.26E-04 3971 0.937116 0.001 1838
0.999686 3.10E-04 4677 0.883215 0.001 2067
0.999304 6.82E-04 3835 0.987707 0.001 1798
7.63E-04 0.99924 4700 0.961702 0.001 2045
0.999011 9.92E-04 3325 0.993381 0.001 1643
0.99914 8.54E-04 3325 0.995272 0.001 1630
0.9991 9.16E-04 3537 0.957447 0.001 1707
0.999028 9.60E-04 3615 0.997163 0.001 1731
8.93E-04 0.999123 2730 0.992435 0.001 1472
0.999166 8.36E-04 3325 0.995272 0.001 1632
0.999046 9.78E-04 4843 0.93948 0.001 2087
0.999391 5.92E-04 4949 0.841135 0.001 2119
0.99933 6.72E-04 2685 0.996217 0.001 1444
0.999647 3.66E-04 5467 0.554137 0.001 2277
0.999209 7.88E-04 4949 0.669976 0.001 2117
5.96E-04 0.999391 4476 0.996217 0.001 1962
0.999027 9.70E-04 4517 0.998109 0.001 1998
9.36E-04 0.999082 5228 0.971158 0.001 2228
0.999367 6.21E-04 3897 0.978723 0.001 1815
0.999618 4.04E-04 3905 0.993381 0.001 1802
0.999335 6.53E-04 3969 0.704965 0.001 1845
9.45E-04 0.999041 5228 0.971631 0.001 2186
0.999314 6.70E-04 3325 0.997636 0.001 1654
0.999654 3.43E-04 6185 0.823168 0.001 2492
0.999038 9.74E-04 3537 0.525296 0.001 1721
0.999644 3.66E-04 5271 0.792435 0.001 2218
0.999468 5.56E-04 5363 0.593381 0.001 2246
0.999011 9.93E-04 4833 0.80331 0.001 2074
8.32E-04 0.999157 5100 0.959338 0.001 2153
0.999605 3.91E-04 5467 0.915839 0.001 2253
7.42E-04 0.999287 3664 0.958392 0.001 1742
0.999568 4.27E-04 3905 0.993381 0.001 1815
0.999396 6.20E-04 3325 0.99669 0.001 1632
0.999068 9.48E-04 2685 0.995272 0.001 1482
9.63E-04 0.999029 5112 0.865248 0.001 2158
0.999104 9.04E-04 3347 0.996217 0.001 1644
0.999212 7.92E-04 3969 0.465721 0.001 1825
0.999129 8.99E-04 3969 0.53617 0.001 1837
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373699
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476650
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493736
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