卷积核一定可以提升网络性能吗？-分类0，2
制作一个网络然后向这个网络上加1-9个卷积核，通过网络的分辨准确率来比较卷积核对网络性能的影响。
网络的结构是
(mnist 0 ,mnist2)81-con(3*3)*n-(49*n)-30-2-(1,0) || (0,1)
分类mnist的0和2，将28*28的图片压缩到9*9，向这个网络增加n个3*3的卷积核，网络隐藏层的节点数是30个，让0向（1，0）收敛，让2向（0，1）收敛。让n分别等于0-9.当n=0时，网络相当于一个三层的网络
(mnist 0 ,mnist2)81-30-2-(1,0) || (0,1)
用这个没有卷积核的3层网络作为参照比较网络性能的变化。
网络迭代的停止标准是：
|网络的输出值-目标函数|<δ,
让δ分别等于0.5到1e-6的34个值每个δ收敛199次，通过每次收敛的准确率的平均值和199次收敛的最大值来比较性能的变化。一共收敛了9*34*199次。
平均性能的表格
| 1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
||
| δ |
平均准确率p-ave |
平均准确率p-ave |
平均准确率p-ave |
平均准确率p-ave |
平均准确率p-ave |
平均准确率p-ave |
平均准确率p-ave |
平均准确率p-ave |
平均准确率p-ave |
平均准确率p-ave |
| 0.5 |
0.5523392 |
0.5039786 |
0.502023 |
0.5121532 |
0.5091611 |
0.5093609 |
0.5166738 |
0.5169935 |
0.5132447 |
0.5188143 |
| 0.4 |
0.9516094 |
0.7559143 |
0.7354191 |
0.7508267 |
0.6995165 |
0.6534412 |
0.6503317 |
0.6558139 |
0.6173537 |
0.649505 |
| 0.3 |
0.9548188 |
0.8792771 |
0.8562769 |
0.8324675 |
0.8165954 |
0.7935727 |
0.7344676 |
0.7343227 |
0.7617811 |
0.7652077 |
| 0.2 |
0.9455553 |
0.9126847 |
0.8743069 |
0.8599209 |
0.8596436 |
0.8487367 |
0.8041924 |
0.7903484 |
0.7557494 |
0.7660594 |
| 0.1 |
0.9601137 |
0.9143681 |
0.8736151 |
0.8528228 |
0.8806283 |
0.8796717 |
0.8396106 |
0.7770588 |
0.7376894 |
0.6889642 |
| 0.01 |
0.9393064 |
0.9245307 |
0.8586921 |
0.7835649 |
0.7043043 |
0.7821613 |
0.8381045 |
0.8263509 |
0.8017723 |
0.7628475 |
| 0.001 |
0.9764878 |
0.9358697 |
0.9156918 |
0.7677628 |
0.677308 |
0.6918139 |
0.6827552 |
0.6992168 |
0.6397644 |
0.6579093 |
| 9.00E-04 |
0.976268 |
0.9418714 |
0.9082715 |
0.7771262 |
0.6896435 |
0.6739163 |
0.6619629 |
0.6649675 |
0.6600498 |
0.6836219 |
| 8.00E-04 |
0.9763554 |
0.9388343 |
0.9299529 |
0.7895916 |
0.6767286 |
0.6480064 |
0.6607965 |
0.6492677 |
0.6606017 |
0.686042 |
| 7.00E-04 |
0.9764379 |
0.937663 |
0.9266137 |
0.8038253 |
0.7073439 |
0.6674051 |
0.6691085 |
0.6738489 |
0.6938295 |
0.748409 |
| 6.00E-04 |
0.9775193 |
0.9359047 |
0.9417115 |
0.842268 |
0.7357039 |
0.6842812 |
0.6739413 |
0.686047 |
0.7140099 |
0.772683 |
| 5.00E-04 |
0.9785883 |
0.9323032 |
0.9336768 |
0.8814125 |
0.7663092 |
0.7402345 |
0.7195346 |
0.761384 |
0.795848 |
0.8459095 |
| 4.00E-04 |
0.9786957 |
0.9397759 |
0.9376979 |
0.9120353 |
0.8389612 |
0.8006309 |
0.7972392 |
0.8230716 |
0.8615768 |
0.8792496 |
| 3.00E-04 |
0.9787057 |
0.9376355 |
0.9369512 |
0.9286942 |
0.8930937 |
0.8779609 |
0.8737425 |
0.9023822 |
0.9043228 |
0.9192658 |
| 2.00E-04 |
0.9807637 |
0.9394288 |
0.9160514 |
0.9294584 |
0.9271831 |
0.9271157 |
0.9328826 |
0.9386095 |
0.9462971 |
0.9588349 |
| 1.00E-04 |
0.980931 |
0.9435847 |
0.9207244 |
0.9284419 |
0.9477307 |
0.9556655 |
0.9600662 |
0.9628136 |
0.9671569 |
0.970661 |
| 9.00E-05 |
0.980409 |
0.9463745 |
0.9198777 |
0.9400207 |
0.949439 |
0.9538298 |
0.9648616 |
0.961882 |
0.9693647 |
0.9697219 |
| 8.00E-05 |
0.9805963 |
0.9482127 |
0.9346559 |
0.9425932 |
0.9509601 |
0.9606856 |
0.9629584 |
0.9673242 |
0.968031 |
0.9698842 |
| 7.00E-05 |
0.9805189 |
0.9511449 |
0.9402679 |
0.9501708 |
0.957721 |
0.9614924 |
0.9692398 |
0.9690101 |
0.9715276 |
0.9699816 |
| 6.00E-05 |
0.9813631 |
0.9534327 |
0.9404153 |
0.9503007 |
0.962876 |
0.9671019 |
0.9707534 |
0.9715576 |
0.9721071 |
0.9730986 |
| 5.00E-05 |
0.9820649 |
0.9538448 |
0.9515645 |
0.9591546 |
0.9651638 |
0.9709657 |
0.9717874 |
0.973698 |
0.9731785 |
0.9733808 |
| 4.00E-05 |
0.9827467 |
0.953645 |
0.9557554 |
0.9651563 |
0.9691499 |
0.9737555 |
0.9740926 |
0.9746471 |
0.9739852 |
0.975219 |
| 3.00E-05 |
0.9830564 |
0.9588249 |
0.964387 |
0.9707584 |
0.9724492 |
0.9763055 |
0.9759883 |
0.9759009 |
0.9736655 |
0.9752915 |
| 2.00E-05 |
0.983526 |
0.9624015 |
0.9721995 |
0.9748769 |
0.975776 |
0.9766801 |
0.9769274 |
0.9769873 |
0.9756062 |
0.9740602 |
| 1.00E-05 |
0.9821723 |
0.9718698 |
0.9794749 |
0.977799 |
0.9770598 |
0.9779814 |
0.9764878 |
0.9773944 |
0.9761531 |
0.974707 |
| 9.00E-06 |
0.9822897 |
0.9740377 |
0.9801942 |
0.9780488 |
0.9786107 |
0.9772221 |
0.9770198 |
0.9759833 |
0.9749343 |
0.9747445 |
| 8.00E-06 |
0.9825819 |
0.9755562 |
0.978888 |
0.9779913 |
0.9770023 |
0.9771671 |
0.9770847 |
0.9765203 |
0.9760507 |
0.9765053 |
| 7.00E-06 |
0.9826743 |
0.9759308 |
0.9779514 |
0.9788755 |
0.9774069 |
0.9771871 |
0.9777766 |
0.9768724 |
0.977859 |
0.9755887 |
| 6.00E-06 |
0.9826368 |
0.9772346 |
0.9790528 |
0.9789454 |
0.9779214 |
0.9770747 |
0.9771497 |
0.9769199 |
0.9764478 |
0.9761681 |
| 5.00E-06 |
0.9826618 |
0.9778415 |
0.9786532 |
0.9784184 |
0.9771247 |
0.978346 |
0.9778215 |
0.9772096 |
0.9766701 |
0.9759308 |
| 4.00E-06 |
0.9829016 |
0.9796822 |
0.9805738 |
0.9779464 |
0.9781812 |
0.9785108 |
0.9785833 |
0.9789554 |
0.9774369 |
0.9764828 |
| 3.00E-06 |
0.9834086 |
0.9801243 |
0.9796522 |
0.9786957 |
0.9785083 |
0.9790953 |
0.979405 |
0.9794799 |
0.9787356 |
0.9779764 |
| 2.00E-06 |
0.9839805 |
0.9801667 |
0.9800768 |
0.9799894 |
0.9800469 |
0.9802292 |
0.9806188 |
0.9806488 |
0.9797047 |
0.9782211 |
| 1.00E-06 |
0.9850945 |
0.9819625 |
0.9821623 |
0.9810084 |
0.9816928 |
0.9825095 |
0.98199 |
0.9821398 |
0.9816953 |
0.9817527 |
没有加卷积核的3层网络的平均准确率大于任何加卷积核的网络，也就是增加卷积核对这个网络的平均性能没有任何正面价值。平均性能随卷积核变化的一个大致的规律，有2个卷积核的网络的平均性能最大，当卷积核数量超过2个以后随着卷积核数量的增加网络的平均性能有明显的下降趋势。
按照平均性能排序如下
81*30*2>2>3>4>5>6>7>8>9>1
再比较网络的最大性能的变化
| 1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
||
| δ |
最大值p-max |
最大值p-max |
最大值p-max |
最大值p-max |
最大值p-max |
最大值p-max |
最大值p-max |
最大值p-max |
最大值p-max |
最大值p-max |
| 0.5 |
0.9284294 |
0.8489066 |
0.7897614 |
0.7659046 |
0.7997018 |
0.888171 |
0.8533797 |
0.833499 |
0.8136183 |
0.8210736 |
| 0.4 |
0.9671968 |
0.97167 |
0.9771372 |
0.972664 |
0.972664 |
0.9741551 |
0.9741551 |
0.9741551 |
0.9761431 |
0.9736581 |
| 0.3 |
0.9686879 |
0.971173 |
0.972664 |
0.972167 |
0.9681909 |
0.973161 |
0.97167 |
0.9686879 |
0.973161 |
0.973161 |
| 0.2 |
0.9627237 |
0.9756461 |
0.971173 |
0.9741551 |
0.9736581 |
0.9746521 |
0.9736581 |
0.972664 |
0.9736581 |
0.97167 |
| 0.1 |
0.9706759 |
0.9691849 |
0.9786282 |
0.97167 |
0.9761431 |
0.9766402 |
0.9756461 |
0.9756461 |
0.9681909 |
0.9671968 |
| 0.01 |
0.9776342 |
0.9751491 |
0.9761431 |
0.9776342 |
0.9761431 |
0.9706759 |
0.972167 |
0.9756461 |
0.9736581 |
0.972167 |
| 0.001 |
0.9816103 |
0.9796223 |
0.9801193 |
0.9741551 |
0.9776342 |
0.9786282 |
0.9791252 |
0.9826044 |
0.9806163 |
0.9801193 |
| 9.00E-04 |
0.9806163 |
0.9786282 |
0.9791252 |
0.9751491 |
0.9771372 |
0.9781312 |
0.9811133 |
0.9806163 |
0.9806163 |
0.9791252 |
| 8.00E-04 |
0.9816103 |
0.9796223 |
0.9811133 |
0.9791252 |
0.9746521 |
0.9801193 |
0.9786282 |
0.9806163 |
0.9806163 |
0.9816103 |
| 7.00E-04 |
0.9816103 |
0.9811133 |
0.9786282 |
0.9781312 |
0.9796223 |
0.9771372 |
0.9781312 |
0.9796223 |
0.9801193 |
0.9831014 |
| 6.00E-04 |
0.9816103 |
0.9791252 |
0.9806163 |
0.9796223 |
0.9771372 |
0.9781312 |
0.972167 |
0.9791252 |
0.9806163 |
0.9801193 |
| 5.00E-04 |
0.9816103 |
0.9791252 |
0.9811133 |
0.9811133 |
0.9811133 |
0.9796223 |
0.9776342 |
0.9816103 |
0.9831014 |
0.9845924 |
| 4.00E-04 |
0.9831014 |
0.9811133 |
0.9791252 |
0.9801193 |
0.9796223 |
0.9811133 |
0.9791252 |
0.9816103 |
0.9826044 |
0.9840954 |
| 3.00E-04 |
0.9835984 |
0.9786282 |
0.9826044 |
0.9811133 |
0.9806163 |
0.9835984 |
0.9831014 |
0.9821074 |
0.9840954 |
0.9845924 |
| 2.00E-04 |
0.9831014 |
0.9845924 |
0.9840954 |
0.9811133 |
0.9831014 |
0.9855865 |
0.9850895 |
0.9850895 |
0.9865805 |
0.9860835 |
| 1.00E-04 |
0.9831014 |
0.9835984 |
0.9835984 |
0.9840954 |
0.9845924 |
0.9865805 |
0.9860835 |
0.9875746 |
0.9875746 |
0.9880716 |
| 9.00E-05 |
0.9845924 |
0.9816103 |
0.9826044 |
0.9860835 |
0.9860835 |
0.9860835 |
0.9870775 |
0.9875746 |
0.9865805 |
0.9875746 |
| 8.00E-05 |
0.9845924 |
0.9835984 |
0.9845924 |
0.9855865 |
0.9865805 |
0.9870775 |
0.9870775 |
0.9875746 |
0.9860835 |
0.9865805 |
| 7.00E-05 |
0.9845924 |
0.9821074 |
0.9831014 |
0.9855865 |
0.9865805 |
0.9875746 |
0.9875746 |
0.9870775 |
0.9870775 |
0.9875746 |
| 6.00E-05 |
0.9845924 |
0.9835984 |
0.9845924 |
0.9855865 |
0.9870775 |
0.9865805 |
0.9875746 |
0.9880716 |
0.9865805 |
0.9875746 |
| 5.00E-05 |
0.9850895 |
0.9826044 |
0.9855865 |
0.9865805 |
0.9880716 |
0.9875746 |
0.9895626 |
0.9875746 |
0.9875746 |
0.9870775 |
| 4.00E-05 |
0.9850895 |
0.9826044 |
0.9855865 |
0.9875746 |
0.9885686 |
0.9875746 |
0.9880716 |
0.9890656 |
0.9875746 |
0.9875746 |
| 3.00E-05 |
0.9855865 |
0.9885686 |
0.9880716 |
0.9875746 |
0.9880716 |
0.9880716 |
0.9880716 |
0.9885686 |
0.9880716 |
0.9880716 |
| 2.00E-05 |
0.9855865 |
0.9880716 |
0.9875746 |
0.9875746 |
0.9900596 |
0.9875746 |
0.9875746 |
0.9890656 |
0.9890656 |
0.9885686 |
| 1.00E-05 |
0.9855865 |
0.9875746 |
0.9880716 |
0.9895626 |
0.9875746 |
0.9885686 |
0.9885686 |
0.9895626 |
0.9890656 |
0.9885686 |
| 9.00E-06 |
0.9850895 |
0.9870775 |
0.9885686 |
0.9880716 |
0.9885686 |
0.9890656 |
0.9885686 |
0.9885686 |
0.9895626 |
0.9885686 |
| 8.00E-06 |
0.9850895 |
0.9865805 |
0.9875746 |
0.9885686 |
0.9895626 |
0.9885686 |
0.9885686 |
0.9885686 |
0.9890656 |
0.9890656 |
| 7.00E-06 |
0.9845924 |
0.9875746 |
0.9885686 |
0.9880716 |
0.9885686 |
0.9880716 |
0.9885686 |
0.9880716 |
0.9880716 |
0.9885686 |
| 6.00E-06 |
0.9845924 |
0.9880716 |
0.9885686 |
0.9885686 |
0.9880716 |
0.9880716 |
0.9885686 |
0.9885686 |
0.9885686 |
0.9900596 |
| 5.00E-06 |
0.9845924 |
0.9875746 |
0.9880716 |
0.9895626 |
0.9890656 |
0.9880716 |
0.9900596 |
0.9890656 |
0.9885686 |
0.9885686 |
| 4.00E-06 |
0.9845924 |
0.9880716 |
0.9885686 |
0.9890656 |
0.9890656 |
0.9880716 |
0.9895626 |
0.9895626 |
0.9885686 |
0.9885686 |
| 3.00E-06 |
0.9855865 |
0.9875746 |
0.9880716 |
0.9890656 |
0.9880716 |
0.9885686 |
0.9895626 |
0.9880716 |
0.9890656 |
0.9900596 |
| 2.00E-06 |
0.9870775 |
0.9875746 |
0.9885686 |
0.9890656 |
0.9885686 |
0.9890656 |
0.9885686 |
0.9895626 |
0.9875746 |
0.9890656 |
| 1.00E-06 |
0.9875746 |
0.9890656 |
0.9900596 |
0.9905567 |
0.9895626 |
0.9915507 |
0.9885686 |
0.9895626 |
0.9895626 |
0.9890656 |
虽然增加卷积核对网路的平均性能没有正面价值，但有卷积核网络的最大性能都好于原始的3层网络，表明卷积核对网络的最大性能有显著提升。
用收敛标准=1e-6的值排序
5>3>2>4=7=8>1=9>6>81*30*2
加上前面做的0-1，0-5，0-6的实验已经做了4组，将所有的数据总结成表格
| 平均性能 | 0-1 | 81*30*2>2>3>4>5>6>7>8>9>1 | 没有正面价值 | ||
| 0-5 | 2>3>4>5>6>7>8>9>81-30-2>1 | 明显提升 | |||
| 0-6 | 81*30*2>2>9>8>1>7>6>5>4>3 | 没有正面价值 | |||
| 0-2 | 81*30*2>2>3>4>5>6>7>8>9>1 | 没有正面价值 | |||
| 最大性能 | 0-1 | 81*30*2=1=2=3=4=5=6=7=8=9 | 没有提升 | ||
| 0-5 | 2>3>4>5>6>7>8>9>1>81-30-2 | 提升明显 | |||
| 0-6 | 81*30*2=2=7>8>1=3=4>6>5 | 无明显提升 | |||
| 0-2 | 5>3>2>4=7=8>1=9>6>81*30*2 | 提升明显 | |||
对网络平均性能有提升的只有0-5，对网络最大性能有明显提升的只有0-5和0-2.而对于0-1和0-6.增加卷积核对网络的平均性能和最大性能都没有任何正面价值。表明卷积核对网络性能的影响不是绝对的，与测试集有很大的关系。并不是所有的测试集都适于加卷积核。
比较迭代次数
| 1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
||
| δ |
迭代次数n |
迭代次数n |
迭代次数n |
迭代次数n |
迭代次数n |
迭代次数n |
迭代次数n |
迭代次数n |
迭代次数n |
迭代次数n |
| 0.5 |
8.8643216 |
16.246231 |
13.809045 |
14.904523 |
15.175879 |
12.135678 |
11.834171 |
11.924623 |
13.065327 |
11.130653 |
| 0.4 |
208.9397 |
1685.9246 |
1134.1307 |
882.45729 |
751.93467 |
664.0804 |
594.83417 |
526.31658 |
481.18593 |
445.8191 |
| 0.3 |
268.72864 |
1923.0955 |
1299.8291 |
1047.608 |
869.26633 |
788.86935 |
699.34171 |
648.02513 |
593.94472 |
548.1809 |
| 0.2 |
324.01508 |
2120.0452 |
1440.6281 |
1160.9749 |
988.66332 |
872.42714 |
790.13568 |
712.67839 |
664.07538 |
630.42211 |
| 0.1 |
410.58794 |
2318.6683 |
1637.1307 |
1291.804 |
1105.392 |
971.68342 |
880.61307 |
814.58794 |
764.27136 |
718.45729 |
| 0.01 |
685.54774 |
2832.7236 |
2076.397 |
1656.4171 |
1424.8995 |
1282.6281 |
1206.7035 |
1101.2513 |
1039.4724 |
999.18593 |
| 0.001 |
1447.1307 |
3803.3216 |
2899.7387 |
2436.8945 |
2234.598 |
2142.9648 |
2086.7085 |
2070.9849 |
2123.7638 |
2199.7889 |
| 9.00E-04 |
1450.9146 |
3783.5276 |
2924.2462 |
2495.8342 |
2291.7538 |
2173.5628 |
2137.2211 |
2139.3467 |
2256.804 |
2365.5226 |
| 8.00E-04 |
1494.9246 |
3831.3266 |
3005.1759 |
2545.6231 |
2314.8342 |
2233.1558 |
2231.5327 |
2247.4975 |
2312.9698 |
2508.6784 |
| 7.00E-04 |
1552.7136 |
3962.8894 |
3032.8241 |
2621.9497 |
2432.1558 |
2312.2362 |
2360.9447 |
2437.2261 |
2532.6633 |
3025.8291 |
| 6.00E-04 |
1711.6281 |
4070.799 |
3151.9698 |
2738.2462 |
2520.1809 |
2427.5578 |
2559.2161 |
2598.5427 |
2897.8191 |
3426.1709 |
| 5.00E-04 |
1968.7136 |
4228.1005 |
3268.7487 |
2908.4422 |
2656.392 |
2614.2312 |
2638.9397 |
3189.1759 |
3705.1457 |
4812.1407 |
| 4.00E-04 |
2165.0553 |
4379.6533 |
3413.5879 |
3049.1709 |
2878.2161 |
2938.7839 |
3379.3819 |
4073.3719 |
5242.4171 |
7248.4221 |
| 3.00E-04 |
2324.0804 |
4616.3869 |
3568.7487 |
3236.6734 |
3262.6985 |
3474.1357 |
4279.6533 |
6453.8191 |
8586.4523 |
11609.673 |
| 2.00E-04 |
2720.8442 |
5050.8744 |
4034.0854 |
3941.7839 |
4741.608 |
5935.9347 |
8696.7739 |
11151.116 |
15180.141 |
19449.472 |
| 1.00E-04 |
3165.7085 |
6349.6985 |
5698.9447 |
6692.2211 |
9883.7035 |
14487.849 |
20128.899 |
26472.593 |
31021.317 |
34488.161 |
| 9.00E-05 |
3407.9095 |
6705.2513 |
6425.6432 |
7671.8342 |
10057.864 |
16786.427 |
22643.764 |
27280.819 |
33107.07 |
36141.161 |
| 8.00E-05 |
3486.7638 |
6955.8141 |
6875.5779 |
8632.7638 |
13695.608 |
18656.789 |
24313.794 |
30661.899 |
32898.834 |
37901.487 |
| 7.00E-05 |
3668.6834 |
7528.9196 |
7582.6533 |
10520.673 |
14594.503 |
21585.935 |
27430.905 |
33478.191 |
37926.065 |
39971.744 |
| 6.00E-05 |
3962.5628 |
8184.2915 |
8908.6583 |
10364.412 |
17772.01 |
26005.452 |
33621.568 |
37461.166 |
40297.015 |
43397.05 |
| 5.00E-05 |
4167.6784 |
8865.0201 |
9484.1759 |
13928.859 |
20620.387 |
29064.899 |
36309.246 |
40110.719 |
43048.417 |
45538.367 |
| 4.00E-05 |
4515.8291 |
9644.5578 |
11563.161 |
18354.367 |
27010.352 |
33951.97 |
40799.658 |
44108.271 |
45917.915 |
48308.945 |
| 3.00E-05 |
5059.8794 |
11485.899 |
15935.819 |
23537.467 |
31983.07 |
40540.769 |
44894.171 |
48028.648 |
49944.307 |
52696.226 |
| 2.00E-05 |
6524.8744 |
15515.141 |
23050.367 |
31693.899 |
39692.101 |
46629.558 |
50373.94 |
54465.653 |
55854.437 |
57249.005 |
| 1.00E-05 |
8527.8995 |
25127.07 |
34262.568 |
43816.402 |
51902.055 |
55930.221 |
60159.965 |
61394.462 |
63830.06 |
66139.307 |
| 9.00E-06 |
8746.4221 |
26410.513 |
37680.035 |
46022.296 |
54010.824 |
57544.231 |
61327.05 |
63699.201 |
64750.774 |
68351 |
| 8.00E-06 |
9474.0101 |
29507.945 |
39589.829 |
48614.332 |
54025.236 |
59733.015 |
62842.513 |
64384.889 |
66818.925 |
68970.196 |
| 7.00E-06 |
10301.668 |
31298.121 |
40019.106 |
50429.01 |
56361.678 |
62009 |
63321.663 |
66217.427 |
69258.372 |
69606.422 |
| 6.00E-06 |
11850.653 |
33845.357 |
44199.276 |
51939.93 |
59028.905 |
63013.839 |
65983.749 |
67526.186 |
69809.583 |
70851.06 |
| 5.00E-06 |
13068.734 |
36965.256 |
47009.317 |
56133.382 |
62123.925 |
66072.719 |
68282.146 |
71326.035 |
72951.161 |
74612.08 |
| 4.00E-06 |
14670.05 |
40347.784 |
49610.824 |
59106.07 |
63852.477 |
69004.804 |
71463.156 |
73956.101 |
77287.442 |
76819.804 |
| 3.00E-06 |
17112.472 |
44845.005 |
56031.894 |
62566.347 |
67823.025 |
73042.563 |
74778.497 |
77379.653 |
80787.312 |
81893.528 |
| 2.00E-06 |
23285.869 |
52058.754 |
62225.432 |
70379.01 |
74180.412 |
77512.573 |
81224.447 |
85341.327 |
87238.95 |
87805.156 |
| 1.00E-06 |
35222.241 |
66830.714 |
77563.397 |
82040.663 |
86059.04 |
90214.985 |
93471.638 |
96062.583 |
99403.251 |
102968.42 |
迭代次数随着卷积核数量的增加而增加，在《计算多卷积核神经网络迭代次数》中已经得到的卷积核的数量与迭代次数的公式
| ni/n(i-1) |
b |
1.1605951 |
1.057724 |
1.0489803 |
1.0482918 |
1.0360988 |
1.027719 |
1.034776 |
1.0358657 |
|
| (i/(i-1))**0.5 |
a |
1.4142136 |
1.2247449 |
1.1547005 |
1.118034 |
1.0954451 |
1.0801234 |
1.069045 |
1.0606602 |
|
| a/b |
α |
1.2185245 |
1.157906 |
1.1007838 |
1.0665294 |
1.0572786 |
1.050991 |
1.0331173 |
1.023936 |
|
| X |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
||
用α和x拟合方程
α=1.3075677654798437*x**-0.11553016292685217
0.9715966417109875 ****** 决定系数 r**2
计算迭代次数
| 2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
|
| α |
1.2069413 |
1.1517078 |
1.1140588 |
1.0857056 |
1.0630759 |
1.0443111 |
1.0283242 |
1.0144261 |
| ∏α |
1.1517078 |
1.2830702 |
1.3930366 |
1.4809036 |
1.546524 |
1.5903281 |
1.6132704 |
|
| 计算 |
82482.182 |
85491.196 |
88036.813 |
90717.416 |
93828.37 |
97543.891 |
101989.61 |
|
| 实测 |
82040.663 |
86059.04 |
90214.985 |
93471.638 |
96062.583 |
99403.251 |
102968.42 |
|
| 误差 |
0.0053817 |
0.0065983 |
0.0241442 |
0.0294659 |
0.0232579 |
0.0187052 |
0.0095059 |
比较耗时
| 1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
||
| δ |
耗时 min/199 |
耗时 min/199 |
耗时 min/199 |
耗时 min/199 |
耗时 min/199 |
耗时 min/199 |
耗时 min/199 |
耗时 min/199 |
耗时 min/199 |
耗时 min/199 |
| 0.5 |
0.06415 |
0.1054333 |
0.1807833 |
0.2866167 |
0.3691167 |
0.4632 |
0.6012167 |
0.68235 |
0.7749 |
0.8857667 |
| 0.4 |
0.0743 |
0.4034333 |
0.5565167 |
0.74555 |
0.8677 |
1.0177333 |
1.2258 |
1.317 |
1.4365333 |
1.5769833 |
| 0.3 |
0.0762833 |
0.4464333 |
0.6118 |
0.8334833 |
0.9509833 |
1.1233167 |
1.3131 |
1.4768 |
1.5941 |
1.7541667 |
| 0.2 |
0.08025 |
0.4785167 |
0.66015 |
0.88305 |
1.0305833 |
1.1935333 |
1.3994 |
1.49935 |
1.6984167 |
1.8722667 |
| 0.1 |
0.0854667 |
0.5151333 |
0.7269333 |
0.9404167 |
1.1488 |
1.2794167 |
1.4967 |
0.6726667 |
1.81285 |
2.0303667 |
| 0.01 |
0.10295 |
0.6091 |
0.8742833 |
1.1268833 |
1.3877833 |
1.5445667 |
1.87975 |
1.98915 |
2.1972667 |
2.4928833 |
| 0.001 |
0.1482167 |
0.7868167 |
1.1538 |
1.5269333 |
1.9254333 |
2.2772667 |
2.8063167 |
3.1866833 |
3.7478167 |
4.4037333 |
| 9.00E-04 |
0.1486833 |
0.7794833 |
1.16055 |
1.5606167 |
1.969 |
2.3014333 |
2.8294333 |
3.2884667 |
3.9535667 |
1.89435 |
| 8.00E-04 |
0.1523833 |
0.7895 |
1.1887833 |
1.58825 |
1.9530167 |
2.3543833 |
2.89775 |
3.4075167 |
3.9764333 |
4.8350333 |
| 7.00E-04 |
0.15595 |
0.8114 |
1.1984167 |
1.62435 |
2.0165333 |
2.4227167 |
3.0292167 |
3.6387333 |
4.3147167 |
5.7025833 |
| 6.00E-04 |
0.1680833 |
0.8324333 |
1.2386 |
1.6883667 |
2.0880667 |
2.5167 |
3.18455 |
3.8301 |
4.8615833 |
6.2168833 |
| 5.00E-04 |
0.1823667 |
0.8596 |
1.2762667 |
1.7719167 |
2.1733333 |
2.6760833 |
3.2791167 |
4.5327833 |
6.0158667 |
8.5344833 |
| 4.00E-04 |
0.1947833 |
0.8926833 |
1.3244833 |
1.8532833 |
0.6664 |
2.9511333 |
4.0198333 |
5.58965 |
8.1587833 |
12.120433 |
| 3.00E-04 |
0.2015833 |
0.9296333 |
1.3770167 |
1.9949667 |
2.5713 |
3.3963333 |
4.93535 |
8.5260333 |
12.716067 |
18.7017 |
| 2.00E-04 |
0.2242667 |
1.07535 |
1.5356333 |
2.4373833 |
3.6019667 |
5.4545 |
7.9383167 |
14.16995 |
21.765317 |
29.262633 |
| 1.00E-04 |
0.2510167 |
1.3040167 |
2.0991 |
3.9648333 |
7.1169 |
12.690533 |
20.795567 |
31.041767 |
43.3882 |
54.798383 |
| 9.00E-05 |
0.2691833 |
1.3718833 |
2.3460667 |
4.2682 |
7.1659667 |
14.644033 |
23.287683 |
34.210233 |
46.565233 |
54.359333 |
| 8.00E-05 |
0.2690667 |
1.41815 |
2.4975833 |
4.7310667 |
9.6323167 |
16.210883 |
24.950917 |
35.979533 |
46.18115 |
59.05395 |
| 7.00E-05 |
0.2797 |
1.5245333 |
2.73555 |
5.70455 |
10.233417 |
18.691283 |
28.08415 |
40.165217 |
53.254283 |
62.658783 |
| 6.00E-05 |
0.29395 |
1.64815 |
3.18345 |
5.6891333 |
12.394317 |
22.412517 |
34.39305 |
43.790267 |
56.792533 |
66.610467 |
| 5.00E-05 |
0.3057167 |
0.7482667 |
3.3784667 |
7.4962667 |
14.319367 |
25.002167 |
38.500217 |
48.504167 |
58.532967 |
71.23395 |
| 4.00E-05 |
0.32555 |
1.90655 |
4.0798 |
8.5269167 |
18.6522 |
29.133117 |
41.577167 |
52.877717 |
63.498467 |
74.2049 |
| 3.00E-05 |
0.3573333 |
2.2570667 |
5.5666667 |
12.17225 |
22.0143 |
34.778783 |
45.4032 |
57.217 |
68.8245 |
83.56915 |
| 2.00E-05 |
0.4487167 |
2.9843667 |
7.9680833 |
16.277983 |
25.902283 |
40.653083 |
50.3917 |
67.005917 |
76.049067 |
87.9441 |
| 1.00E-05 |
0.5657 |
4.6324 |
12.738767 |
22.3845 |
35.64345 |
48.739217 |
61.586 |
70.405867 |
87.764633 |
101.07163 |
| 9.00E-06 |
0.5779 |
4.8042167 |
13.97685 |
23.499033 |
37.245083 |
48.107167 |
61.408567 |
75.853533 |
89.590633 |
106.89732 |
| 8.00E-06 |
0.6237333 |
5.3426 |
13.776983 |
24.87535 |
35.31665 |
50.382933 |
64.300433 |
77.051283 |
91.333683 |
105.29282 |
| 7.00E-06 |
0.6701667 |
5.6624333 |
11.376483 |
26.492033 |
38.42935 |
53.487317 |
64.339583 |
78.093217 |
96.4085 |
107.67667 |
| 6.00E-06 |
-1.043133 |
6.1118333 |
15.4332 |
27.433567 |
40.1873 |
52.854733 |
67.0226 |
79.72825 |
94.623517 |
109.36867 |
| 5.00E-06 |
0.8278667 |
6.6677333 |
16.386617 |
27.391317 |
42.329833 |
57.257867 |
69.3746 |
84.96685 |
100.74403 |
115.31163 |
| 4.00E-06 |
0.92955 |
7.2674 |
17.24095 |
30.8037 |
46.6526 |
57.598467 |
74.86045 |
88.066183 |
106.69952 |
118.55075 |
| 3.00E-06 |
1.0805333 |
8.0748 |
19.4551 |
32.2475 |
44.217 |
62.321083 |
78.466417 |
92.807433 |
112.71635 |
126.68888 |
| 2.00E-06 |
1.4429333 |
9.3541 |
22.58695 |
38.047467 |
52.679083 |
66.735667 |
82.635983 |
100.66913 |
121.80912 |
135.0271 |
| 1.00E-06 |
2.15025 |
11.933783 |
24.124117 |
40.834483 |
57.550183 |
77.955 |
97.3552 |
113.19602 |
138.3318 |
158.15827 |
计算迭代时间的表达式
用同样的办法
| α=1.3075677654798437*x**-0.11553016292685217 |
|
|||||||
| 2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
|
| α |
1.2069413 |
1.1517078 |
1.1140588 |
1.0857056 |
1.0630759 |
1.0443111 |
1.0283242 |
1.0144261 |
| ∏α |
1.1517078 |
1.2830702 |
1.3930366 |
1.4809036 |
1.546524 |
1.5903281 |
1.6132704 |
|
| 计算 |
38.480969 |
53.179713 |
68.454014 |
84.646016 |
102.14023 |
121.35416 |
142.7457 |
|
| 实测 |
40.834483 |
57.550183 |
77.955 |
97.3552 |
113.19602 |
138.3318 |
158.15827 |
|
| 误差 |
0.0576355 |
0.0759419 |
0.1218778 |
0.1305445 |
0.0976694 |
0.1227313 |
0.0974503 |
综合上面的4个表格
卷积核并不是对所有的训练集都有正面价值，并不是所有的网络都可以通过增加卷积核的办法提升网络性能。