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卷积核的数量是不是越多越好？-分类0，5

發布時間：2025/4/5 编程问答 60 豆豆

生活随笔收集整理的這篇文章主要介紹了卷积核的数量是不是越多越好？-分类0，5 小編覺得挺不錯的,現在分享給大家,幫大家做個參考.

制作一個二分類的網絡來分類mnist的0和5，并向網絡上加卷積核從1個核到9個核。

網絡的結構是

(mnist 0 ,mnist9)81-con(3*3)*n-(49*n)-30-2-(1,0) || (0,1)

將mnist的28*28的圖片壓縮到9*9，用n個3*3的卷積核，節點數分別為n*49，30，2。讓0向（1，0）收斂，讓5向（0，1）收斂，讓n分別等于1-9.

與Tensorflow不同的是這個網絡通過網絡的輸出值與目標函數的絕對誤差判斷是否停止：

if (Math.abs(網路輸出值[0]-目標函數[0])< δ? &&? Math.abs(網絡輸出值[1]-目標函數[1])< δ?? )，

其中δ分別等于0.5到1e-6.的34個值。對應每個δ收斂199次，分別記錄與之對應的迭代次數，收斂時間，并計算199次的平均準確率和199次的最大準確率，來比較這10個網絡的性能差異。

首先比較最大準確率

?	81-30-2	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	0.7948718	0.6730769	0.7692308	0.7638889	0.8178419	0.7820513	0.8012821	0.9027778	0.8904915	0.767094
0.4	0.9428419	0.9636752	0.9604701	0.9620727	0.9529915	0.9583333	0.9610043	0.9636752	0.9465812	0.9519231
0.3	0.9524573	0.9711538	0.9679487	0.9594017	0.9599359	0.9513889	0.9487179	0.9567308	0.9444444	0.9428419
0.2	0.9626068	0.9647436	0.9604701	0.9604701	0.9535256	0.9465812	0.9481838	0.9540598	0.9508547	0.9444444
0.1	0.9674145	0.9674145	0.9647436	0.9652778	0.957265	0.957265	0.9540598	0.9503205	0.954594	0.9449786
0.01	0.9695513	0.9700855	0.971688	0.9684829	0.9679487	0.9668803	0.9684829	0.9732906	0.9706197	0.971688
0.001	0.9775641	0.9727564	0.9797009	0.9807692	0.9807692	0.9807692	0.9786325	0.9818376	0.9823718	0.980235
9.00E-04	0.9775641	0.9738248	0.9813034	0.9818376	0.9807692	0.9813034	0.9807692	0.9823718	0.9818376	0.9818376
8.00E-04	0.9775641	0.9754274	0.9807692	0.9813034	0.9823718	0.9823718	0.982906	0.9823718	0.982906	0.9818376
7.00E-04	0.9770299	0.971688	0.9818376	0.982906	0.9834402	0.982906	0.982906	0.9818376	0.982906	0.9834402
6.00E-04	0.9770299	0.9727564	0.982906	0.982906	0.9839744	0.9834402	0.9823718	0.9839744	0.9834402	0.9823718
5.00E-04	0.9759615	0.9754274	0.9823718	0.9839744	0.9850427	0.9823718	0.9839744	0.9834402	0.9839744	0.9839744
4.00E-04	0.974359	0.9748932	0.9834402	0.982906	0.9839744	0.9839744	0.9845085	0.9845085	0.9850427	0.9850427
3.00E-04	0.980235	0.9770299	0.9845085	0.9866453	0.9845085	0.9839744	0.9855769	0.9850427	0.9855769	0.9850427
2.00E-04	0.980235	0.9754274	0.9855769	0.9861111	0.9866453	0.9850427	0.9850427	0.9861111	0.9861111	0.9866453
1.00E-04	0.9807692	0.9786325	0.9866453	0.9877137	0.9866453	0.9850427	0.9861111	0.9866453	0.9861111	0.9850427
9.00E-05	0.9807692	0.9775641	0.9861111	0.9871795	0.9877137	0.9871795	0.9855769	0.9866453	0.9861111	0.9855769
8.00E-05	0.9807692	0.9764957	0.9887821	0.9882479	0.9871795	0.9882479	0.9866453	0.9877137	0.9861111	0.9855769
7.00E-05	0.9807692	0.9775641	0.9887821	0.9877137	0.9877137	0.9903846	0.9871795	0.9877137	0.9871795	0.9871795
6.00E-05	0.9813034	0.9791667	0.9882479	0.9877137	0.9887821	0.9903846	0.9882479	0.9877137	0.9866453	0.9871795
5.00E-05	0.9807692	0.9780983	0.9898504	0.9887821	0.9882479	0.9898504	0.9882479	0.9877137	0.9866453	0.9882479
4.00E-05	0.9797009	0.9775641	0.9893162	0.9898504	0.9898504	0.9903846	0.9887821	0.9887821	0.9866453	0.9866453
3.00E-05	0.9797009	0.980235	0.9898504	0.9887821	0.9893162	0.9925214	0.9887821	0.9877137	0.9882479	0.9882479
2.00E-05	0.9791667	0.9813034	0.991453	0.9887821	0.9909188	0.991453	0.9898504	0.9882479	0.9882479	0.9877137
1.00E-05	0.9813034	0.9845085	0.9903846	0.9903846	0.9903846	0.991453	0.9909188	0.9887821	0.9882479	0.9893162
9.00E-06	0.9813034	0.9845085	0.9909188	0.991453	0.9903846	0.9919872	0.9893162	0.9893162	0.9893162	0.9903846
8.00E-06	0.9813034	0.982906	0.991453	0.991453	0.9903846	0.9925214	0.9893162	0.9893162	0.9893162	0.991453
7.00E-06	0.9813034	0.9850427	0.9909188	0.9925214	0.9909188	0.9919872	0.991453	0.9898504	0.9898504	0.9909188
6.00E-06	0.9818376	0.9839744	0.9893162	0.991453	0.9925214	0.991453	0.9893162	0.9893162	0.9909188	0.9909188
5.00E-06	0.9818376	0.9850427	0.991453	0.9935897	0.991453	0.9925214	0.9909188	0.9903846	0.9903846	0.9893162
4.00E-06	0.9813034	0.9866453	0.9935897	0.9930556	0.9930556	0.9925214	0.9919872	0.9898504	0.991453	0.9898504
3.00E-06	0.9834402	0.9855769	0.9930556	0.9930556	0.9919872	0.9919872	0.9925214	0.9909188	0.9903846	0.9909188
2.00E-06	0.9861111	0.9871795	0.9951923	0.9935897	0.9925214	0.9930556	0.991453	0.9909188	0.9925214	0.9919872
1.00E-06	0.9882479	0.9893162	0.9951923	0.9946581	0.9930556	0.9935897	0.9941239	0.9903846	0.9909188	?

2>3>4>5>6>7>8>9>1>81-30-2

隨著卷積核數量的增加網絡的最大性能先上升，當n=2時最大性能到達頂點，然后隨著卷積核數量的增加最大性能開始下降。最大準確率曲線是一條開口向下有極大值的曲線。原始的未加卷積核的網絡81-30-2的最大性能小于1個卷積核的網絡。

2.比較平均性能

?	81-30-2	1	81-30-2	2	3	4	5	6	7	8	9
δ	平均準確率p-ave	平均準確率p-ave	平均準確率p-ave	平均準確率p-ave	平均準確率p-ave	平均準確率p-ave	平均準確率p-ave	平均準確率p-ave	平均準確率p-ave	平均準確率p-ave	平均準確率p-ave
0.5	0.5335518	0.5023891	0.5335518	0.5185033	0.5139963	0.5152579	0.5164525	0.5226802	0.5270476	0.5192549	0.5158699
0.4	0.9188652	0.715718	0.9188652	0.7907755	0.7566518	0.7538386	0.7368949	0.7321302	0.7023123	0.7222598	0.6953357
0.3	0.9282497	0.8465807	0.9282497	0.8974064	0.882833	0.8699158	0.8699427	0.8521937	0.8459364	0.8328636	0.8368579
0.2	0.9465731	0.8818961	0.9465731	0.9070137	0.9074754	0.9003296	0.8978654	0.891082	0.8898338	0.8887493	0.8770884
0.1	0.9638148	0.904426	0.9638148	0.925815	0.9159687	0.9124039	0.9085143	0.9077116	0.9094189	0.9066137	0.8981714
0.01	0.9656402	0.9115181	0.9656402	0.9400233	0.9378436	0.9272887	0.9153996	0.9101195	0.8945395	0.8760925	0.8632318
0.001	0.9733148	0.903261	0.9733148	0.9617666	0.9538988	0.9504923	0.9438888	0.9492763	0.9436687	0.9433546	0.9410273
9.00E-04	0.9732342	0.9092202	0.9732342	0.9617237	0.959391	0.9572381	0.9567335	0.9579441	0.9562127	0.9571683	0.957469
8.00E-04	0.9731752	0.9105597	0.9731752	0.9651006	0.9609909	0.9641101	0.9621747	0.9644993	0.9630471	0.963235	0.9646228
7.00E-04	0.9728396	0.9110295	0.9728396	0.9668911	0.9688614	0.9664025	0.9659999	0.9659167	0.9660213	0.9664857	0.9644107
6.00E-04	0.972088	0.9108899	0.972088	0.9669609	0.9673796	0.9678467	0.9679487	0.9702761	0.968491	0.9676266	0.9660562
5.00E-04	0.9712746	0.91475	0.9712746	0.9700989	0.9727215	0.9699244	0.9717149	0.9708988	0.9723618	0.9699405	0.9666226
4.00E-04	0.9715672	0.9135072	0.9715672	0.9737657	0.9747079	0.9734731	0.9730758	0.971782	0.9713095	0.969111	0.9680266
3.00E-04	0.9745925	0.9094457	0.9745925	0.9743348	0.9740825	0.9747589	0.9730946	0.973347	0.9713632	0.9704801	0.9671756
2.00E-04	0.9764313	0.9190611	0.9764313	0.9768259	0.9767105	0.9756475	0.9741845	0.97361	0.9727269	0.9698841	0.9705338
1.00E-04	0.974861	0.9286497	0.974861	0.9799156	0.9793089	0.9780527	0.9642336	0.9778218	0.9756877	0.9740288	0.9741818
9.00E-05	0.9772608	0.9331299	0.9772608	0.9800069	0.9808605	0.9800767	0.9738677	0.9778057	0.9762783	0.9761038	0.9755294
8.00E-05	0.9783614	0.9257693	0.9783614	0.9808605	0.9806753	0.9799559	0.9765226	0.9771158	0.9773332	0.9777574	0.9743321
7.00E-05	0.9792284	0.9319353	0.9792284	0.9824067	0.9817651	0.9803397	0.9770541	0.9789546	0.9781359	0.9778862	0.9747938
6.00E-05	0.9795801	0.9219656	0.9795801	0.9829301	0.982557	0.9810135	0.9779963	0.9785681	0.9776339	0.9779855	0.9760931
5.00E-05	0.9791157	0.9300589	0.9791157	0.9827771	0.9829543	0.9817973	0.9796579	0.979172	0.9777976	0.978509	0.9770031
4.00E-05	0.9788687	0.9251224	0.9788687	0.9837918	0.9830295	0.9820577	0.9800901	0.9799666	0.9777332	0.9780849	0.9777252
3.00E-05	0.9788338	0.9216435	0.9788338	0.9843529	0.9833382	0.982761	0.9820014	0.9799344	0.9789143	0.9786701	0.978458
2.00E-05	0.9787935	0.9209053	0.9787935	0.9851286	0.9840307	0.9831154	0.9828738	0.9804498	0.9805196	0.9794861	0.9773708
1.00E-05	0.9783506	0.9308052	0.9783506	0.9853568	0.9842133	0.983816	0.9837516	0.9821114	0.980788	0.9797116	0.9699244
9.00E-06	0.9782352	0.9433573	0.9782352	0.9860467	0.9852763	0.9841811	0.9850052	0.982251	0.9801438	0.9727725	0.9775775
8.00E-06	0.9781332	0.9331621	0.9781332	0.9865191	0.9859098	0.9846642	0.9850266	0.9828496	0.9793895	0.979062	0.9805652
7.00E-06	0.9782218	0.9447344	0.9782218	0.9865809	0.9862749	0.9853568	0.9853326	0.9829677	0.9815289	0.9789224	0.9794861
6.00E-06	0.9785761	0.9488844	0.9785761	0.977454	0.9866238	0.985687	0.985789	0.9829946	0.981145	0.979419	0.9815343
5.00E-06	0.9790861	0.9555389	0.9790861	0.9850374	0.9871016	0.9865594	0.9858937	0.9835368	0.9823423	0.98078	0.9820201
4.00E-06	0.9797331	0.9611895	0.9797331	0.9869164	0.9868413	0.9859554	0.9862158	0.9849837	0.9821651	0.9823557	0.9758005
3.00E-06	0.9802726	0.9644107	0.9802726	0.9879257	0.9874587	0.9862212	0.9863312	0.9837865	0.9817812	0.9826214	0.980525
2.00E-06	0.9802673	0.9657878	0.9802673	0.9891149	0.9884841	0.9874587	0.9860682	0.9850749	0.9843126	0.9839502	0.9819557
1.00E-06	0.9832684	0.9736745	0.9832684	0.9895203	0.9887123	0.9878694	0.9870292	0.9842428	0.9839717	0.9848495	?

2>3>4>5>6>7>8>9>81-30-2>1

平均性能也是一條開口向下有極大值的曲線，頂點在n=2，當n>2后隨著卷積核的數量的增加網絡的平均性能下降，原始的無卷積核的網絡81-30-2的平均性能在9與1之間。

3.比較迭代次數

?	81-30-2	1	2	3	4	5	6	7	8	9
δ	迭代次數n	迭代次數n	迭代次數n	迭代次數n	迭代次數n	迭代次數n	迭代次數n	迭代次數n	迭代次數n	迭代次數n
0.5	9.160804	18.492462	17.311558	15.743719	15.432161	16.346734	16.798995	15.236181	12.527638	13.844221
0.4	259.27136	2288.4774	298.56281	236.08543	220.34171	208.94472	193.33668	194.93467	195.60302	173.17588
0.3	344.46734	2537.2513	154.73367	144.78392	140.98995	135.54774	139.46734	136.20603	131.78392	131.72864
0.2	447.94472	2826.4472	163.06533	145.31156	138.14573	142.52261	136.78392	142.35678	140.75377	138.37688
0.1	546.02513	2872.4774	182.70352	173.42714	163.88442	170.22111	166.94975	168.49749	167.97487	173.1005
0.01	855.83417	3587.7035	444.98995	492.43719	505.8794	518.46734	518.61307	526.22111	532.85427	519.0201
0.001	1523.0955	4529.1508	1436.4573	1692.8995	1901.6985	1920.9749	1954.9246	1907.5427	1998.1508	2039.1859
9.00E-04	1575.8392	4472.7688	1420.0452	1642.4271	1883.0754	1885.6432	1929.2211	1934.4422	2052.6181	2154.0955
8.00E-04	1657.0251	4529.0201	1491.2211	1749.6633	1986.2864	1940.6734	2014.0603	1949.593	2134.1055	2116.809
7.00E-04	1715.8492	4662.1859	1720.3116	1978.9397	2035.6985	2038.5678	2104	2170.8995	2200.1709	2291.8492
6.00E-04	1784.804	4733.206	1785.5176	2072.6784	2165.9045	2188.9648	2285.201	2195.0503	2285.4774	2328.201
5.00E-04	1845.3668	4991.0704	1883.0553	2268.2965	2208.1809	2327.2211	2576.4874	2318.2412	2525.1106	2651.4774
4.00E-04	1991.608	5026.5327	2050.1407	2512.005	2483.1608	2693.8141	2735.6633	2599.0804	2686.9799	2738.3216
3.00E-04	2476.593	5324.7889	2454.9648	2812.5779	2978.3568	2997.9598	3013.9196	3046.5678	2939.8643	3272.7186
2.00E-04	2879.5678	5625.8291	2968.9749	3547.2362	3372.3266	3704.4573	3738.206	3535.6583	3900.3668	3865.3216
1.00E-04	3656.4422	6300.1558	4395.1256	4725.9799	4806.7839	5709.2111	5251.6784	5196.9196	5116.0603	5670.2764
9.00E-05	3871.3367	6393.1809	4249.2563	4417.4121	4810.5578	5519.6834	5046.7487	5074.6633	5158.3317	5383.608
8.00E-05	4092.3317	6461.5829	4191.804	4866.4171	4839.5729	5669.0302	5343.5427	5026.0251	5428.9749	5520.8291
7.00E-05	4245.1759	6737.6583	4436.8945	4731.3266	5093.0653	5754.7688	5417.1307	5070.9347	5305.6784	5715.3668
6.00E-05	4380.0302	7093.3367	4591.5427	4900.6734	4905.6784	6238.9397	5270.2764	5661.1055	5813.1759	5918.1759
5.00E-05	4537.1457	7531.8392	4661.5176	5124.5126	5510.2161	6595.4372	5685.5729	5880.8844	6158.4221	6208.3467
4.00E-05	4640.3417	7936.4372	4891.402	5516.794	6135.8945	7171.2362	6050.5879	6745.5779	6235.9447	6859.3417
3.00E-05	4661.799	9152.593	5546.593	6137.5879	6695.1457	8369.5477	6584.7889	6960.005	7222.0101	7973.3216
2.00E-05	4680.2814	11076.392	6096.0151	7011.6583	7930.3819	10117.513	7742.3618	8165.598	9197.5678	9313.9598
1.00E-05	5416.9347	21376.387	7696.7487	9081.0251	10953.553	13592.714	10608.905	11073.864	12293.543	26256.166
9.00E-06	5648.5528	23626.186	7388.9196	9121.7035	11644.558	13399.03	10421.492	11290.291	25672.563	28369.141
8.00E-06	5854.5126	22841.116	7448.0352	10441.879	12020.628	14497.573	10589.693	12226.497	24378.653	31408.497
7.00E-06	6466.8241	27217.905	8028.1106	10264.025	11424.136	15736	11804.558	12377.161	28071.392	32087.392
6.00E-06	7063.5276	29178.518	13824.156	10173.643	12465.075	14446.613	12330.91	13247.849	27945.513	31361.045
5.00E-06	8079.7688	32877.06	17375.497	10970.327	13638.372	15564.558	13761.432	15111.543	28023.92	31892.99
4.00E-06	9789.3668	38820.513	18059.628	10907.543	12893.543	16265.276	13874.136	15322.347	30350.889	43566.864
3.00E-06	12842.985	42096.995	17814.714	12538.608	14224.08	18842.116	15662.533	17281.513	33741	44026.899
2.00E-06	15130.764	45979.095	19888.558	14292.839	18005.352	23446.09	19432.312	20531.422	37886.804	53454.829
1.00E-06	22746.784	57660.256	22674.513	18676.844	25511.854	31373.98	46643.784	25671.94	58280.683	?

1>2>81-30-2>3<4<5<6<8<9

除了第7條曲線迭代次數大體上是開口向上有極小值的曲線，當n=3時有極小值，原始網絡的迭代次數在2和3之間。

4.最后比較收斂時間

?	81-30-2	1	2	3	4	5	6	7	8	9
δ	耗時 min/199	耗時 min/200	耗時 min/201	耗時 min/202	耗時 min/203	耗時 min/204	耗時 min/205	耗時 min/206	耗時 min/207	耗時 min/208
0.5	3.74165	2.81195	2.3356167	2.3812833	2.41515	2.9003	2.6563167	2.6037	2.84575	2.8494333
0.4	4.1931333	5.6846	2.7243167	2.70495	2.7242	3.2568667	2.9844	2.9688167	3.2323833	3.3662333
0.3	4.3721667	6.0041333	2.5128	2.6714667	2.6016333	3.1050333	2.8730167	2.8513333	3.10375	3.19675
0.2	1.8564	6.3734667	2.5300833	2.5535333	2.58635	3.0920333	2.86965	2.8619667	3.1330667	1.5324833
0.1	4.6676167	6.4437667	2.5244667	2.58445	2.62775	3.1427833	2.9340667	2.9192333	3.1962167	3.3199833
0.01	5.2088833	7.3604833	2.8403333	-0.58325	3.2821333	3.7929667	3.6354833	3.6760833	4.0656333	4.34505
0.001	6.3676667	8.5501167	4.1040667	4.81455	5.6036	6.4232333	6.4751	6.5676833	4.7247667	8.4573333
9.00E-04	6.4966833	8.4807667	4.0384333	4.78085	4.4528667	6.3847167	6.43075	6.62765	7.4476667	8.8073
8.00E-04	6.7034	8.5498333	4.0271167	4.902	5.7288833	6.3719	6.6306333	6.7841833	7.6419833	8.7222167
7.00E-04	6.75745	8.7181	4.40325	5.2740167	5.7882833	6.6370667	4.7293	7.3665333	7.8004667	9.1840167
6.00E-04	6.8404	8.80775	4.4865833	5.37725	5.97655	6.9272667	7.19365	7.2842667	7.9979	9.2548
5.00E-04	6.96285	9.1413333	4.6114333	5.5824167	6.0483333	4.6591833	7.75675	7.63485	8.5834667	4.68755
4.00E-04	7.2862	9.1793667	2.0086833	5.9000167	6.5423833	7.85475	8.1671333	8.3504167	8.9637333	10.353083
3.00E-04	5.5816667	9.5621333	5.3298333	6.3522167	7.35655	8.4423167	8.7433667	9.4013667	9.5543167	11.735717
2.00E-04	8.71945	9.94355	5.9963833	7.6161167	7.98195	9.8058333	10.35895	7.4389833	11.78	12.985167
1.00E-04	10.0387	10.8083	7.8396667	6.6227	8.08105	13.075883	10.481833	13.735567	14.89045	18.13725
9.00E-05	10.4178	10.913867	7.6425667	8.9246333	10.598233	12.833733	13.65385	13.224017	15.28805	12.19625
8.00E-05	10.768333	11.00325	7.6148167	9.4912	10.727383	13.143783	14.537217	13.125967	12.697583	17.459533
7.00E-05	11.03965	11.351883	7.8541833	9.2809	11.121167	13.43135	14.232983	13.212067	15.641267	17.93935
6.00E-05	10.08165	11.803567	8.0594	9.40985	10.833517	11.598917	13.148567	14.464867	16.658133	14.880983
5.00E-05	11.744183	11.558367	8.1961	9.7128333	11.570617	15.025217	9.5606	14.932633	17.066967	19.808217
4.00E-05	11.89545	13.316783	6.1954	10.30215	9.7710167	16.06235	14.383	16.738283	15.5816	21.337733
3.00E-05	11.962133	15.221267	9.3110833	8.50165	13.173067	15.182917	15.406933	15.875433	19.50825	17.410333
2.00E-05	11.947567	17.901783	10.009533	12.496633	15.301883	21.597833	17.651783	19.716983	24.296367	25.427117
1.00E-05	11.3564	28.662117	12.06555	15.452733	20.2102	27.94395	23.37535	25.817217	30.363617	66.972917
9.00E-06	13.647017	32.964433	11.656317	15.563267	19.6612	24.301083	23.03795	26.32625	62.753017	73.75985
8.00E-06	14.8104	31.809883	9.7350667	14.798517	21.876733	29.526217	20.793	29.18205	56.518383	78.87625
7.00E-06	19.61805	36.461317	12.50245	16.808383	19.1992	28.709183	25.774433	27.901667	65.33635	82.823083
6.00E-06	20.565867	32.756467	19.5214	16.716867	22.1395	29.47665	25.1147	32.154583	66.056617	78.91485
5.00E-06	20.4579	39.824267	23.790033	17.896783	23.918717	28.025917	30.541083	33.241817	67.500017	81.8001
4.00E-06	26.225617	43.384617	25.791183	17.6785	24.153483	31.654933	28.107583	34.056233	71.296233	112.10312
3.00E-06	30.387583	47.09945	25.603317	19.945967	26.7583	35.474967	34.3632	39.994767	80.6607	113.52042
2.00E-06	38.4815	50.440683	23.894317	20.51575	30.430383	43.04565	39.376083	46.21565	87.64325	148.69013
1.00E-06	52.468967	64.474033	32.313933	28.640433	46.072367	57.658567	93.034733	56.501933	133.82317	?

1>81-30-2>2>3<4<5<6<8<9

與迭代次數類似曲線也是開口向上的曲線，當n=3時耗時最少

所以綜合上面的4個表格

增加卷積核網絡最大性能先升后降有極大值，超過極大值隨著卷積核數量的增加最大性能下降

增加卷積核網絡的平均性能也先升后降有極大值。超過極大值網絡的平均性能同樣會下降

迭代次數曲線近似為一條開口向上的曲線有極小值，這個極小值對應的n與1和2的極大值對應的n接近

收斂時間與迭代次數有正比關系

最大性能：2>3>4>5>6>7>8>9>1>81-30-2

平均性能：2>3>4>5>6>7>8>9>81-30-2>1

迭代次數：1>2>81-30-2>3<4<5<6<8<9

收斂時間：1>81-30-2>2>3<4<5<6<8<9

因此對于這個網絡無論更在乎最大性能還是平均性能都應該選擇2個卷積核，因為n=2同時是這個網絡在1-9個卷積核內平均性能和最大性能的極大值。

如果更在乎收斂效率也可以選擇3個卷積核，3比2的性能稍差但需要的迭代次數只有2的82%，可以節省些時間。

如果選擇了8個卷積核當收斂標準δ=1e-6的時候要比2個卷積核多付出2.57倍的計算量但最大性能比n=2的網絡還是要差0.5%.因此就這個網絡來說卷積核的數量肯定不是越多越好，當卷積核的數量超過2個以后卷積核的數量對網絡的性能已經沒有任何正面價值，而且數量越多越慢性能也越差。

實驗數據

學習率r=0.1

權重初始化方式

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);

第一層第二層和卷積核的權重的初始化的x分別為1000,1000,200

總結

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编程问答

卷积核的数量是不是越多越好？-分类0，5

總結