制作一個5層網絡和一個3層網絡

圖中左邊的5層網絡很顯然可以看作是由兩個右邊的3層網絡組合而成，所以左邊的網絡的迭代次數和右邊的網絡的迭代次數有什么關系？

在《測量一組5層網絡的迭代次數》測量了輸入固定為0.1時5層網絡和3層網絡收斂迭代次數之間的關系。本文測量當輸入是0-1之間的隨機數時對迭代次數的影響。

將左邊的5層網絡寫成

（r）-3*6*3*6*3-(3*k)，k∈{0,1}

意思是向網絡輸入0到1的隨機數r，輸出是1，0，0。

右邊的3層網絡寫成

（r）-3*6*3-(3*k)，k∈{0,1}

其中r的初始化方法是

Random rand1 =new Random();

int ti1=rand1.nextInt(98)+1;

r=((double)ti1/100);

?

用這種辦法制作了三組網絡

（r）-2-10-2-10-2-（2*k），k∈{0,1}

（r）-2-10-2-（2*k），k∈{0,1}

?

（r）-3-10-3-10-3-（3*k），k∈{0,1}

（r）-3-10-3-（3*k），k∈{0,1}

?

（r）-4-10-4-10-4-（4*k），k∈{0,1}

（r）-4-10-4-（4*k），k∈{0,1}

用來比較

（r）-x-10-x-10-x-（x*k），k∈{0,1}

（r）-x-10-x-（x*k），k∈{0,1}

這兩類網絡迭代次數的關系。

具體的實驗過程

網絡的收斂標準是

if (Math.abs(f4[0]-y[0])< δ? &&? Math.abs(f4[1]-y[1])< δ? &&? Math.abs(f4[2]-y[2])< δ? )

因為對應每個收斂標準δ都有一個特征的迭代次數n與之對應因此可以用迭代次數曲線n(δ)來評價網絡性能。

具體進樣順序	?	?	?
δ=0.5	迭代次數		?
r	1	判斷是否達到收斂
梯度下降	?	?	?
r	2	判斷是否達到收斂
梯度下降	?	?	?
……	?	?	?
每當網路達到收斂標準記錄迭代次數
將這一過程重復199次		?	?
δ=0.4	?	?	?
……	?	?	?
δ=1e-7	?	?	?

本文嘗試了δ從1e-7到0.5的共35個值，當網絡滿足條件收斂是記錄迭代次數

首先觀察5層網絡迭代次數的變化

?	r-2-10-2-10-2	r-3-10-3-10-3	r-4-10-4-10-4		?	?	?	0.1-2-10-2-10-2	0.1-3-10-3-10-3	0.1-4-10-4-10-4		?	?	?	?	?
δ	迭代次數n	迭代次數n	迭代次數n	r2/r2	r3/r2	r4/r2	δ	迭代次數n	迭代次數n	迭代次數n	0.1-2/0.1-2	0.1-3/0.12	0.1-4/0.1-2	r-2/0.1-2	r-3/0.1-3	r-4/0.1-4
0.5	1.904522613	1.869346734	3	1	0.981530343	1.575197889	0.5	1.934673367	1.959798995	3	1	1.012987013	1.550649351	0.984415584	0.953846154	1
0.4	5.341708543	5.331658291	7	1	0.998118532	1.310442145	0.4	5.371859296	5.341708543	7	1	0.994387278	1.303086997	0.994387278	0.998118532	1
0.3	10.34673367	10.22613065	12	1	0.988343856	1.159786304	0.3	10.27135678	10.18090452	11	1	0.991193738	1.070939335	1.007338552	1.004442251	1.090909091
0.2	18.70854271	18.30653266	20	1	0.978511953	1.069030352	0.2	18.66331658	18.52763819	20	1	0.99273021	1.071620894	1.002423263	0.988066178	1
0.1	40.25125628	38.37688442	38	1	0.953433208	0.944069913	0.1	40.10050251	38.36683417	39	1	0.956766917	0.972556391	1.003759399	1.000261952	0.974358974
0.01	325.8040201	268.6934673	235	1	0.824708876	0.721292512	0.01	325.6130653	268.5025126	237	1	0.824606078	0.727857771	1.000586447	1.000711184	0.991561181
0.001	2279.577889	1680.497487	1398	1	0.737196783	0.613271434	0.001	2280.115578	1684.38191	1406	1	0.738726548	0.616635408	0.999764183	0.997693859	0.9943101
1.00E-04	15912.24623	12004.45729	10590	1	0.75441626	0.665525146	1.00E-04	15835.47739	12056.8191	10609	1	0.76138021	0.669951384	1.004847902	0.995657079	0.998209068
9.00E-05	17423.96482	13189.36181	11673	1	0.756966738	0.669939369	9.00E-05	17342.0603	13253.43719	11717	1	0.764236599	0.675640598	1.004722883	0.995165376	0.996244773
8.00E-05	19282.14573	14663.1608	13043	1	0.760452753	0.676428868	8.00E-05	19208.1206	14739.47739	13077	1	0.767356562	0.680805805	1.003853846	0.9948223	0.997400015
7.00E-05	21687.30653	16544.53266	14783	1	0.762867101	0.681642968	7.00E-05	21562.95477	16633.47236	14844	1	0.771391145	0.688402872	1.005766917	0.994652969	0.995890596
6.00E-05	24788.30151	19034.80402	17128	1	0.767894646	0.690971102	6.00E-05	24676.82412	19138.08543	17197	1	0.775548966	0.696888705	1.004517493	0.994603357	0.995987672
5.00E-05	29117.86935	22492.8392	20378	1	0.772475449	0.699845162	5.00E-05	28974.85427	22613.09045	20480	1	0.780438453	0.706819776	1.004935834	0.994682228	0.995019531
4.00E-05	35437.03015	27629.55779	25185	1	0.779680399	0.710697254	4.00E-05	35289.97487	27783.49749	25306	1	0.787291507	0.717087504	1.004167055	0.994459312	0.995218525
3.00E-05	45809.18593	36073.57286	33209	1	0.787474654	0.724941937	3.00E-05	45565.71357	36295.21106	33318	1	0.796546531	0.731207686	1.005343324	0.993893459	0.996728495
2.00E-05	65988.56784	52783.36181	49229	1	0.799886458	0.746023161	2.00E-05	65556.1005	53122.43719	49468	1	0.810335526	0.754590337	1.006596905	0.993617097	0.995168594
1.00E-05	124218.0452	102095.9497	97192	1	0.821909164	0.782430603	1.00E-05	123256.9196	102725.196	97258	1	0.833423359	0.789067261	1.007797742	0.99387447	0.999321393
9.00E-06	136835.3719	112952.5578	107913	1	0.825463155	0.788633805	9.00E-06	135849.9447	113718.9146	108216	1	0.837092093	0.796584792	1.007253792	0.993260956	0.997200044
8.00E-06	152610.0352	126633.8995	121242	1	0.829787499	0.794456274	8.00E-06	151453.6382	127307.3518	121518	1	0.840569783	0.802344542	1.00763532	0.994710028	0.997728732
7.00E-06	172631.2513	143969.5829	138604	1	0.833971728	0.802890548	7.00E-06	171409.3065	144868.4121	139247	1	0.845160715	0.812365459	1.007128812	0.993795547	0.995382306
6.00E-06	199241.1256	167092.598	161638	1	0.838645122	0.811268254	6.00E-06	197814.8894	168071.0653	161784	1	0.849638093	0.817855524	1.007209954	0.994178253	0.999097562
5.00E-06	236247.5427	199373.392	193006	1	0.843917315	0.816965111	5.00E-06	234464.0503	200668.6884	193666	1	0.855861221	0.825994432	1.007606677	0.9935451	0.996592071
4.00E-06	291092.995	247769.0754	240321	1	0.851168113	0.825581529	4.00E-06	288965.3467	249317.9447	241691	1	0.862795306	0.836401329	1.007362988	0.993787574	0.994331605
3.00E-06	381793.9749	328359.2915	320678	1	0.860043147	0.839924203	3.00E-06	378816.8543	330180.2663	322513	1	0.871609229	0.851369194	1.007858997	0.994484907	0.994310307
2.00E-06	560602.8794	488816	481138	1	0.871947002	0.858251032	2.00E-06	556550.6985	491437.4623	482398	1	0.883005742	0.866763803	1.007280884	0.994665726	0.997388049
1.00E-06	1088196.508	969244.1558	962243	1	0.890688537	0.884254813	1.00E-06	1080983.186	974155.5427	964074	1	0.901175481	0.89184921	1.006672927	0.994958313	0.998100768
9.00E-07	1203908.025	1075569.915	1068340	1	0.893398742	0.88739337	9.00E-07	1208133.623	1080634.106	1072751	1	0.894465716	0.887940688	0.996502375	0.995313686	0.995888142
8.00E-07	1348428.719	1209469.04	1202423	1	0.89694696	0.891721589	8.00E-07	1353904.367	1215191.643	1206559	1	0.897546143	0.891170033	0.995955661	0.995290781	0.99657207
7.00E-07	1534027.111	1380826.05	1375220	1	0.900131451	0.896476985	7.00E-07	1539773.07	1387027.477	1381749	1	0.900799932	0.897371845	0.996268308	0.99552898	0.995274829
6.00E-07	1780354.844	1610095.312	1601415	1	0.904367642	0.899492034	6.00E-07	1786506.508	1616167.709	1606431	1	0.904652573	0.899202434	0.996556596	0.996242719	0.99687755
5.00E-07	2125035.533	1929071.94	1927719	1	0.907783381	0.907146714	5.00E-07	2133555.362	1937818.854	1931582	1	0.90825806	0.905334839	0.996006746	0.995486207	0.998000085
4.00E-07	2637184.055	2409909.352	2410357	1	0.913819173	0.913988918	4.00E-07	2646545.613	2418741.794	2414921	1	0.913924091	0.9124804	0.996462726	0.996348332	0.998110083
3.00E-07	3488346.553	3209632.161	3220585	1	0.920101289	0.923241126	3.00E-07	3500985.513	3222108.256	3226161	1	0.920343213	0.921500814	0.996389885	0.996127971	0.99827163
2.00E-07	5184203.387	4811423.382	4829216	1	0.928093098	0.931525181	2.00E-07	5199169.422	4828569.704	4839273	1	0.928719438	0.930778093	0.997121457	0.996448985	0.997921795
1.00E-07	1.02E+07	9627046.156	9688361	1	0.943828055	0.949839314	1.00E-07	1.03E+07	9653872.432	9700878	1	0.937269168	0.941832816	0.990291262	0.99722119	0.998709704

?

很明顯當δ<0.1時

r-2-10-2-10-2 ?> ?r-3-10-3-10-3 ?> ?r-4-10-4-10-4

也就是輸入層節點數越少迭代次數越多。

與當輸入固定為0.1的數據比較

當δ<0.01時

這組數據同樣滿足輸入層節點數越少迭代次數越多的規律。

再比較隨機輸入與固定輸入迭代次數之間的比例關系（r）-x-10-x-10-x-（x*k），k∈{0,1}? ???/?? 0.1-x-10-x-10-x-（x*k），k∈{0,1}

從圖中明顯的觀察到隨機輸入網絡（r）-x-10-x-10-x-（x*k），k∈{0,1}的迭代次數是要稍小于固定輸入網絡0.1-x-10-x-10-x-（x*k），k∈{0,1}的迭代次數。隨機輸入網絡的迭代次數大概要比固定輸入0.1的網絡的迭代次數要少0.5%。

?

?

?

?

?

?

?

?

?

?

然后再比較3層網絡的數據

?	r-2-10-2	r-3-10-3	r-4-10-4	?	?	?	?	0.1-2-10-2	0.1-3-10-3	0.1-4-10-4	?	?	?	?	?	?
δ	迭代次數n	迭代次數n	迭代次數n	r2/r2	r3/r2	r4/r2	δ	迭代次數n	迭代次數n	迭代次數n	0.1-2/0.1-2	0.1-3/0.12	0.1-4/0.1-2	r-2/0.1-2	r-3/0.1-3	r-4/0.1-4
0.5	1.869346734	2.040201005	3	1	1.091397849	1.604838709	0.5	1.834170854	2.150753769	4	1	1.17260274	2.180821918	1.019178083	0.948598131	0.75
0.4	5.316582915	5.577889447	7	1	1.049149338	1.316635161	0.4	5.467336683	5.668341709	7	1	1.036764706	1.280330882	0.972426471	0.984042553	1
0.3	10.34673367	10.4321608	12	1	1.008256435	1.159786304	0.3	10.3718593	10.52763819	12	1	1.015019379	1.156976744	0.997577519	0.990930787	1
0.2	18.5678392	18.48241206	20	1	0.995399188	1.077131258	0.2	18.98492462	19.09547739	21	1	1.005823187	1.106140815	0.978030704	0.967894737	0.952380952
0.1	39.7839196	38.00502513	42	1	0.955286093	1.055702918	0.1	41.81407035	41.92964824	43	1	1.002764091	1.028361976	0.951448143	0.906399808	0.976744186
0.01	326.8140704	281.0904523	347	1	0.860092872	1.061765791	0.01	405.9145729	400.9246231	397	1	0.987706897	0.978038303	0.805130173	0.701105485	0.874055416
0.001	2555.768844	1994.175879	2942	1	0.78026457	1.151121318	0.001	3909.160804	3785.150754	3660	1	0.968277066	0.936262329	0.653789642	0.526841864	0.803825137
1.00E-04	21902.38693	16359.0402	23884	1	0.74690673	1.090474754	1.00E-04	38025.66332	35887.74372	33662	1	0.943776928	0.885244255	0.575989609	0.455839195	0.709524092
9.00E-05	23880.13568	18649.9598	37315	1	0.780982154	1.562595812	9.00E-05	42192.34171	39777.31156	37338	1	0.94276141	0.884947327	0.565982705	0.468859233	0.999384006
8.00E-05	27373.11558	20427.95477	26333	1	0.74627803	0.962002295	8.00E-05	47395.70352	44621.80905	41802	1	0.941473715	0.881978679	0.57754424	0.45780203	0.629945936
7.00E-05	30471.34171	23575.8593	46405	1	0.77370598	1.522906357	7.00E-05	54052.65327	50836.96482	47443	1	0.940508222	0.877718246	0.563734431	0.463754266	0.978121114
6.00E-05	33747.86935	26453.01508	43844	1	0.783842524	1.299163498	6.00E-05	62950.38191	59079.18593	55259	1	0.938504011	0.877818344	0.536102694	0.44775524	0.793427315
5.00E-05	41400.74372	31641.95477	57098	1	0.764284695	1.379153968	5.00E-05	75340.25628	70575.01005	65584	1	0.936750332	0.870504074	0.549516895	0.448345027	0.870608685
4.00E-05	50904.86935	38461.94975	63706	1	0.755565238	1.251471634	4.00E-05	93917.48744	87725.11558	81242	1	0.934065827	0.865035918	0.542016942	0.438437151	0.784151055
3.00E-05	68669.09045	51233.01508	102785	1	0.746085535	1.496816098	3.00E-05	124764.8342	116155.4975	107253	1	0.930995486	0.859641266	0.550388183	0.441072667	0.958341492
2.00E-05	98491.42211	74906.1206	131495	1	0.760534461	1.335090886	2.00E-05	186176.5779	172366.9598	158480	1	0.925825159	0.851234896	0.529021552	0.434573544	0.829726148
1.00E-05	193272.4774	144249.7286	203355	1	0.746354217	1.0521674	1.00E-05	368710.8643	338333.7889	308464	1	0.917612747	0.836601331	0.524184384	0.426353304	0.65925035
9.00E-06	206907.4774	158850.8543	296829	1	0.767738587	1.434597743	9.00E-06	408984.0804	375023.1005	341247	1	0.916962587	0.83437722	0.505905944	0.423576185	0.869836218
8.00E-06	237203.201	180718.5427	363607	1	0.761872276	1.532892467	8.00E-06	459388.5276	420561.8643	382394	1	0.915481861	0.832397801	0.516345504	0.429707394	0.950870045
7.00E-06	269616.4372	202380.0402	421455	1	0.75062204	1.563165081	7.00E-06	524038.9497	478899.1457	435268	1	0.913861739	0.830602382	0.514496942	0.422594281	0.968265528
6.00E-06	308451.3015	240200	405253	1	0.778729086	1.313831383	6.00E-06	609813.0704	556314.598	504206	1	0.912270702	0.826820586	0.505812874	0.431770083	0.803744898
5.00E-06	366383.0905	279138.8844	438198	1	0.761877094	1.196010437	5.00E-06	729919.3065	664199.0553	600920	1	0.909962306	0.823269086	0.50195013	0.420263899	0.729211875
4.00E-06	441795.6482	344849.6935	733902	1	0.780563808	1.661179785	4.00E-06	909130.6181	825530.8241	744853	1	0.908044243	0.819302513	0.485953987	0.417730851	0.98529777
3.00E-06	587062.9196	459529.0302	747879	1	0.78275942	1.273933296	3.00E-06	1207124.558	1091742.894	980381	1	0.904416107	0.812162252	0.486331684	0.420913232	0.762845261
2.00E-06	881343.3819	672539.7437	1245116	1	0.763084806	1.412747886	2.00E-06	1799195.739	1619069.065	1449669	1	0.899884893	0.805731677	0.489854085	0.415386692	0.858896755
1.00E-06	1691667.427	1292568.874	2046819	1	0.764079779	1.209941722	1.00E-06	3559300.412	3175825.116	2818734	1	0.892261048	0.791934839	0.475280879	0.407002535	0.726148335
9.00E-07	1861192.327	1469246.196	2010564	1	0.789411269	1.080255904	9.00E-07	3947484.774	3517441.141	3123123	1	0.891058824	0.791167839	0.471488159	0.417703136	0.643767152
8.00E-07	2080246.372	1621744.271	2166817	1	0.779592404	1.041615565	8.00E-07	4433661.618	3943425.156	3493458	1	0.889428535	0.787939699	0.469193762	0.411252707	0.620249907
7.00E-07	2464329.97	1797949.739	3092601	1	0.72958969	1.254945984	7.00E-07	5056311.834	4490016.492	3972648	1	0.888002291	0.785680973	0.487376976	0.40043277	0.778473451
6.00E-07	2925796.92	2091886.859	2729326	1	0.714980197	0.93284875	6.00E-07	5882920.477	5216202.241	4605197	1	0.886668834	0.782807964	0.497337493	0.401036379	0.59266216
5.00E-07	3289760.563	2467525.844	3698517	1	0.750062443	1.124251121	5.00E-07	7040336.97	6225307.176	5492530	1	0.884234264	0.780151578	0.467273168	0.396370135	0.673372198
4.00E-07	4007427.271	3216941.045	5727957	1	0.802744711	1.429335235	4.00E-07	8766715.804	7731842.281	6800941	1	0.881954252	0.775768389	0.457118419	0.416063976	0.842230068
3.00E-07	5245088.296	4254122.518	6026219	1	0.811067856	1.14892613	3.00E-07	1.16E+07	1.02E+07	8973350	1	0.879310345	0.773564655	0.452162784	0.417070835	0.671568478
2.00E-07	7786895.241	5986582.417	1.07E+07	1	0.768802229	1.374103499	2.00E-07	1.73E+07	1.52E+07	1.32E+07	1	0.878612717	0.76300578	0.450109552	0.393854106	0.810606061
1.00E-07	1.58E+07	1.23E+07	2.71E+07	1	0.778481013	1.715189873	1.00E-07	3.42E+07	2.97E+07	2.58E+07	1	0.868421053	0.754385965	0.461988304	0.414141414	1.050387597

?

3層隨機輸入數據比較

r-2-10-2>r-3-10-3但r-4-10-4的數據變的不規則

3層固定輸入的數據

這個明顯的0.1-2-10-2>0.1-3-10-3>0.1-4-10-4,輸入節點數越少迭代次數越多。

比較隨機輸入和固定輸入的比值

若不考慮r-4和0.1-4的值

r-2/0.1-2? > r-3/0.1-3 的值但都接近0.5.

再比較3層網絡和5層網絡的迭代次數的比值

?	r-2-10-2	r-3-10-3	r-4-10-4	?	0.1-2-10-2	0.1-3-10-3	0.1-4-10-4
δ	r-2-10-2-10-2	r-3-10-3-10-3	r-4-10-4-10-4	δ	0.1-2-10-2-10-2	0.1-3-10-3-10-3	0.1-4-10-4-10-4
0.5	0.981530343	1.091397849	1	0.5	0.948051948	1.097435898	1.333333333
0.4	0.995296331	1.046182846	1	0.4	1.01777362	1.061147695	1
0.3	1	1.02014742	1	0.3	1.009784737	1.034057256	1.090909091
0.2	0.992479184	1.009607467	1	0.2	1.017232095	1.030648224	1.05
0.1	0.988389513	0.990310331	1.105263158	0.1	1.04273183	1.092861821	1.102564103
0.01	1.003100178	1.046138022	1.476595745	0.01	1.246616356	1.493187603	1.675105485
0.001	1.121158815	1.186658055	2.104434907	0.001	1.714457303	2.247204587	2.603129445
1.00E-04	1.376448467	1.36274717	2.255335222	1.00E-04	2.401295672	2.976551562	3.172966349
9.00E-05	1.37053397	1.414015331	3.196693224	9.00E-05	2.432948622	3.001282685	3.186651873
8.00E-05	1.419609413	1.393148111	2.018937361	8.00E-05	2.467482608	3.027367109	3.196604726
7.00E-05	1.405031172	1.424993971	3.139078671	7.00E-05	2.506736848	3.056305005	3.196106171
6.00E-05	1.361443394	1.389718279	2.559785147	6.00E-05	2.55099204	3.086995622	3.213293016
5.00E-05	1.421832869	1.406756812	2.801943272	5.00E-05	2.600194485	3.12098031	3.20234375
4.00E-05	1.436488022	1.392058101	2.529521541	4.00E-05	2.661307858	3.157454011	3.210384889
3.00E-05	1.499024465	1.420236783	3.095094703	3.00E-05	2.738129713	3.200298169	3.219070773
2.00E-05	1.492552806	1.419123717	2.67108818	2.00E-05	2.839958089	3.244711066	3.203687232
1.00E-05	1.555913049	1.412883949	2.092301836	1.00E-05	2.991400933	3.293581342	3.171605421
9.00E-06	1.512090584	1.406350218	2.750632454	9.00E-06	3.010557577	3.297807597	3.153387669
8.00E-06	1.554309326	1.42709451	2.999018492	8.00E-06	3.033195723	3.303515927	3.146809526
7.00E-06	1.561805497	1.405713875	3.040713111	7.00E-06	3.057237442	3.305752709	3.125869857
6.00E-06	1.548130691	1.437526275	2.507164157	6.00E-06	3.082746057	3.309996263	3.1165381
5.00E-06	1.550844027	1.400080932	2.270385377	5.00E-06	3.113139543	3.309928722	3.102867824
4.00E-06	1.517713088	1.391818947	3.053840488	4.00E-06	3.146157934	3.311156865	3.081840035
3.00E-06	1.537643227	1.39947016	2.332180567	3.00E-06	3.186565076	3.306505583	3.03981855
2.00E-06	1.572134954	1.375854603	2.587856291	2.00E-06	3.232761622	3.294557679	3.005130618
1.00E-06	1.55456061	1.333584388	2.127133167	1.00E-06	3.29265104	3.260080117	2.923773486
9.00E-07	1.545958901	1.366016449	1.881951439	9.00E-07	3.267423983	3.254978833	2.911321453
8.00E-07	1.542718827	1.34087291	1.80204221	8.00E-07	3.274722887	3.245105559	2.895389285
7.00E-07	1.606444862	1.30208272	2.248804555	7.00E-07	3.283803265	3.237150357	2.875086575
6.00E-07	1.643378526	1.299231694	1.704321491	6.00E-07	3.292974557	3.227512969	2.866725679
5.00E-07	1.548096732	1.279125881	1.918597576	5.00E-07	3.299814523	3.212533082	2.843539648
4.00E-07	1.519585735	1.334880518	2.376393621	4.00E-07	3.312512643	3.196638145	2.816216762
3.00E-07	1.503602987	1.325423695	1.871156638	3.00E-07	3.313352757	3.165629206	2.781432793
2.00E-07	1.502042775	1.244243531	2.215680558	2.00E-07	3.32745456	3.147930118	2.727682443
1.00E-07	1.549019608	1.277650465	2.79717075	1.00E-07	3.32038835	3.07648565	2.659553084

?

隨機輸入3層網絡的迭代次數是對應5層網絡的迭代次數的大約1.5倍左右

固定輸入3層網絡的迭代次數是對應5層網絡的迭代次數的3倍左右。

這組實驗表明3層網絡隨機輸入的迭代次數比固定輸入的迭代次數要小的多，甚至小于后者的50%。

而5層網絡的隨機輸入的迭代次數只比固定輸入的迭代次數略小，二者相差甚至小于1%,也就是5層網絡對輸入數據并不敏感。

?

實驗數據

學習率 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)ti1/1000);

?

?

dr-2-10-2-10-2?? ?　?? ?　?? ?　?? ?　?? ?　?? ?　
??? ?　?? ?　?? ?　?? ?　?? ?　?? ?　?? ?　
f2[0]?? ?f2[1]?? ?迭代次數n?? ?平均準確率p-ave?? ?δ?? ?耗時ms/次?? ?耗時ms/199次?? ?耗時 min/199
0.527608315?? ?0.472339838?? ?1.904522613?? ?0?? ?0.5?? ?0.412060302?? ?82?? ?0.001366667
0.619745309?? ?0.379034134?? ?5.341708543?? ?0?? ?0.4?? ?0.391959799?? ?78?? ?0.0013
0.712427458?? ?0.285357852?? ?10.34673367?? ?0?? ?0.3?? ?0.155778894?? ?31?? ?0.000516667
0.806808233?? ?0.19310006?? ?18.70854271?? ?0?? ?0.2?? ?0.236180905?? ?47?? ?0.000783333
0.902011379?? ?0.097776184?? ?40.25125628?? ?0?? ?0.1?? ?0.688442211?? ?142?? ?0.002366667
0.990027433?? ?0.009968527?? ?325.8040201?? ?0?? ?0.01?? ?3.015075377?? ?600?? ?0.01
0.999000529?? ?1.00E-03?? ?2279.577889?? ?0?? ?0.001?? ?15.2361809?? ?3047?? ?0.050783333
0.999900012?? ?1.00E-04?? ?15912.24623?? ?0?? ?1.00E-04?? ?91.9798995?? ?18304?? ?0.305066667
0.999910011?? ?9.00E-05?? ?17423.96482?? ?0?? ?9.00E-05?? ?98.45728643?? ?19609?? ?0.326816667
0.999920009?? ?8.00E-05?? ?19282.14573?? ?0?? ?8.00E-05?? ?109.1306533?? ?21733?? ?0.362216667
0.999930008?? ?7.00E-05?? ?21687.30653?? ?0?? ?7.00E-05?? ?122.4623116?? ?24370?? ?0.406166667
0.999940006?? ?6.00E-05?? ?24788.30151?? ?0?? ?6.00E-05?? ?140.6532663?? ?27990?? ?0.4665
0.999950004?? ?5.00E-05?? ?29117.86935?? ?0?? ?5.00E-05?? ?164.2462312?? ?32700?? ?0.545
0.999960004?? ?4.00E-05?? ?35437.03015?? ?0?? ?4.00E-05?? ?199.3969849?? ?39696?? ?0.6616
0.999970003?? ?3.00E-05?? ?45809.18593?? ?0?? ?3.00E-05?? ?259.8492462?? ?51720?? ?0.862
0.999980002?? ?2.00E-05?? ?65988.56784?? ?0?? ?2.00E-05?? ?375.7236181?? ?74778?? ?1.2463
0.999990001?? ?1.00E-05?? ?124218.0452?? ?0?? ?1.00E-05?? ?703.4974874?? ?140005?? ?2.333416667
0.999991001?? ?9.00E-06?? ?136835.3719?? ?0?? ?9.00E-06?? ?761.959799?? ?151639?? ?2.527316667
0.999992001?? ?8.00E-06?? ?152610.0352?? ?0?? ?8.00E-06?? ?861.0150754?? ?171358?? ?2.855966667
0.999993001?? ?7.00E-06?? ?172631.2513?? ?0?? ?7.00E-06?? ?972.6281407?? ?193560?? ?3.226
0.999994001?? ?6.00E-06?? ?199241.1256?? ?0?? ?6.00E-06?? ?1125.437186?? ?223962?? ?3.7327
0.999995001?? ?5.00E-06?? ?236247.5427?? ?0?? ?5.00E-06?? ?1336.170854?? ?265906?? ?4.431766667
0.999996001?? ?4.00E-06?? ?291092.995?? ?0?? ?4.00E-06?? ?1650.854271?? ?328528?? ?5.475466667
0.999997?? ?3.00E-06?? ?381793.9749?? ?0?? ?3.00E-06?? ?2128.020101?? ?423493?? ?7.058216667
0.999998?? ?2.00E-06?? ?560602.8794?? ?0?? ?2.00E-06?? ?3168.80402?? ?630608?? ?10.51013333
0.999999?? ?1.00E-06?? ?1088196.508?? ?0?? ?1.00E-06?? ?6009.703518?? ?1195947?? ?19.93245
0.9999991?? ?9.00E-07?? ?1203908.025?? ?0?? ?9.00E-07?? ?6628.276382?? ?1319028?? ?21.9838
0.9999992?? ?8.00E-07?? ?1348428.719?? ?0?? ?8.00E-07?? ?7429.336683?? ?1478438?? ?24.64063333
0.9999993?? ?7.00E-07?? ?1534027.111?? ?0?? ?7.00E-07?? ?8488.236181?? ?1689160?? ?28.15266667
0.9999994?? ?6.00E-07?? ?1780354.844?? ?0?? ?6.00E-07?? ?10088.17588?? ?2007549?? ?33.45915
0.9999995?? ?5.00E-07?? ?2125035.533?? ?0?? ?5.00E-07?? ?11910.59799?? ?2370210?? ?39.5035
0.9999996?? ?4.00E-07?? ?2637184.055?? ?0?? ?4.00E-07?? ?14739.1809?? ?2933128?? ?48.88546667
0.9999997?? ?3.00E-07?? ?3488346.553?? ?0?? ?3.00E-07?? ?19689.76382?? ?3918288?? ?65.3048
0.9999998?? ?2.00E-07?? ?5184203.387?? ?0?? ?2.00E-07?? ?29417.75377?? ?5854135?? ?97.56891667
0.9999999?? ?1.00E-07?? ?1.02E+07?? ?0?? ?1.00E-07?? ?54512.9598?? ?10848096?? ?180.8016
　?? ?　?? ?　?? ?　?? ?　?? ?　?? ?　?? ?　
　?? ?　?? ?　?? ?　?? ?　?? ?　?? ?　?? ?607.63275

dr-3-10-3-10-3?? ??? ??? ??? ??? ??? ??? ?
?? ??? ??? ??? ??? ??? ??? ??? ?
f2[0]?? ?f2[1]?? ?f2[2]?? ?迭代次數n?? ?平均準確率p-ave?? ?δ?? ?耗時ms/次?? ?耗時ms/199次?? ?耗時 min/199
0.526440336?? ?0.472256796?? ?0.472675861?? ?1.869346734?? ?0?? ?0.5?? ?0.472361809?? ?109?? ?0.001816667
0.620190597?? ?0.378660228?? ?0.381783874?? ?5.331658291?? ?0?? ?0.4?? ?0.155778894?? ?31?? ?0.000516667
0.713801296?? ?0.285566484?? ?0.287647306?? ?10.22613065?? ?0?? ?0.3?? ?0.236180905?? ?63?? ?0.00105
0.806969107?? ?0.192119984?? ?0.194035873?? ?18.30653266?? ?0?? ?0.2?? ?0.236180905?? ?47?? ?0.000783333
0.902155298?? ?0.097590075?? ?0.097948187?? ?38.37688442?? ?0?? ?0.1?? ?0.472361809?? ?94?? ?0.001566667
0.99004019?? ?0.009950875?? ?0.009949246?? ?268.6934673?? ?0?? ?0.01?? ?2.834170854?? ?564?? ?0.0094
0.99900074?? ?9.99E-04?? ?9.99E-04?? ?1680.497487?? ?0?? ?0.001?? ?13.06030151?? ?2600?? ?0.043333333
0.999900025?? ?1.00E-04?? ?1.00E-04?? ?12004.45729?? ?0?? ?1.00E-04?? ?78.81909548?? ?15700?? ?0.261666667
0.999910028?? ?9.00E-05?? ?9.00E-05?? ?13189.36181?? ?0?? ?9.00E-05?? ?77.22613065?? ?15383?? ?0.256383333
0.999920025?? ?8.00E-05?? ?8.00E-05?? ?14663.1608?? ?0?? ?8.00E-05?? ?85.75376884?? ?17067?? ?0.28445
0.999930019?? ?7.00E-05?? ?7.00E-05?? ?16544.53266?? ?0?? ?7.00E-05?? ?97.30150754?? ?19378?? ?0.322966667
0.999940016?? ?6.00E-05?? ?6.00E-05?? ?19034.80402?? ?0?? ?6.00E-05?? ?113.3366834?? ?22554?? ?0.3759
0.999950014?? ?5.00E-05?? ?5.00E-05?? ?22492.8392?? ?0?? ?5.00E-05?? ?133.7688442?? ?26620?? ?0.443666667
0.999960011?? ?4.00E-05?? ?4.00E-05?? ?27629.55779?? ?0?? ?4.00E-05?? ?163.120603?? ?32476?? ?0.541266667
0.999970009?? ?3.00E-05?? ?3.00E-05?? ?36073.57286?? ?0?? ?3.00E-05?? ?211.8140704?? ?42151?? ?0.702516667
0.999980007?? ?2.00E-05?? ?2.00E-05?? ?52783.36181?? ?0?? ?2.00E-05?? ?309.5376884?? ?61614?? ?1.0269
0.999990003?? ?1.00E-05?? ?1.00E-05?? ?102095.9497?? ?0?? ?1.00E-05?? ?599.5125628?? ?119303?? ?1.988383333
0.999991002?? ?9.00E-06?? ?9.00E-06?? ?112952.5578?? ?0?? ?9.00E-06?? ?670.1407035?? ?133358?? ?2.222633333
0.999992002?? ?8.00E-06?? ?8.00E-06?? ?126633.8995?? ?0?? ?8.00E-06?? ?757.7085427?? ?150784?? ?2.513066667
0.999993002?? ?7.00E-06?? ?7.00E-06?? ?143969.5829?? ?0?? ?7.00E-06?? ?875.2311558?? ?174172?? ?2.902866667
0.999994002?? ?6.00E-06?? ?6.00E-06?? ?167092.598?? ?0?? ?6.00E-06?? ?1003.854271?? ?199767?? ?3.32945
0.999995002?? ?5.00E-06?? ?5.00E-06?? ?199373.392?? ?0?? ?5.00E-06?? ?1183.085427?? ?235434?? ?3.9239
0.999996001?? ?4.00E-06?? ?4.00E-06?? ?247769.0754?? ?0?? ?4.00E-06?? ?1512.356784?? ?300976?? ?5.016266667
0.999997001?? ?3.00E-06?? ?3.00E-06?? ?328359.2915?? ?0?? ?3.00E-06?? ?1945.427136?? ?387140?? ?6.452333333
0.999998001?? ?2.00E-06?? ?2.00E-06?? ?488816?? ?0?? ?2.00E-06?? ?2507.829146?? ?499074?? ?8.3179
0.999999?? ?1.00E-06?? ?1.00E-06?? ?969244.1558?? ?0?? ?1.00E-06?? ?5722.160804?? ?1138710?? ?18.9785
0.9999991?? ?9.00E-07?? ?9.00E-07?? ?1075569.915?? ?0?? ?9.00E-07?? ?6469.055276?? ?1287342?? ?21.4557
0.9999992?? ?8.00E-07?? ?8.00E-07?? ?1209469.04?? ?0?? ?8.00E-07?? ?7172.030151?? ?1427234?? ?23.78723333
0.9999993?? ?7.00E-07?? ?7.00E-07?? ?1380826.05?? ?0?? ?7.00E-07?? ?8186.537688?? ?1629128?? ?27.15213333
0.9999994?? ?6.00E-07?? ?6.00E-07?? ?1610095.312?? ?0?? ?6.00E-07?? ?9600.678392?? ?1910545?? ?31.84241667
0.9999995?? ?5.00E-07?? ?5.00E-07?? ?1929071.94?? ?0?? ?5.00E-07?? ?11807.44221?? ?2349681?? ?39.16135
0.9999996?? ?4.00E-07?? ?4.00E-07?? ?2409909.352?? ?0?? ?4.00E-07?? ?14563.61809?? ?2898167?? ?48.30278333
0.9999997?? ?3.00E-07?? ?3.00E-07?? ?3209632.161?? ?0?? ?3.00E-07?? ?20176.22111?? ?4015080?? ?66.918
0.9999998?? ?2.00E-07?? ?2.00E-07?? ?4811423.382?? ?0?? ?2.00E-07?? ?28304.31156?? ?5632558?? ?93.87596667
0.9999999?? ?1.00E-07?? ?1.00E-07?? ?9627046.156?? ?0?? ?1.00E-07?? ?56264.20603?? ?11196577?? ?186.6096167
?? ??? ??? ??? ??? ??? ??? ??? ?
?? ??? ??? ??? ??? ??? ??? ??? ?599.0246833

dr-4-10-4-10-4?? ??? ??? ??? ??? ??? ??? ??? ?
?? ??? ??? ??? ??? ??? ??? ??? ??? ?
f2[0]?? ?f2[1]?? ?f2[2]?? ?f2[3]?? ?迭代次數n?? ?平均準確率p-ave?? ?δ?? ?耗時ms/次?? ?耗時ms/199次?? ?耗時 min/199
0.526261475?? ?0.470138296?? ?0.473433824?? ?0.470196578?? ?3?? ?0?? ?0.5?? ?0.72361809?? ?144?? ?0.0024
0.619481649?? ?0.377210594?? ?0.378727222?? ?0.378469657?? ?7?? ?0?? ?0.4?? ?0.155778894?? ?47?? ?0.000783333
0.713542792?? ?0.285969096?? ?0.285711052?? ?0.286628343?? ?12?? ?0?? ?0.3?? ?0.316582915?? ?63?? ?0.00105
0.808050416?? ?0.191568156?? ?0.192380965?? ?0.19206912?? ?20?? ?0?? ?0.2?? ?0.391959799?? ?78?? ?0.0013
0.902437476?? ?0.097327741?? ?0.097530188?? ?0.097340394?? ?38?? ?0?? ?0.1?? ?0.467336683?? ?109?? ?0.001816667
0.990051834?? ?0.00994703?? ?0.009956631?? ?0.009950273?? ?235?? ?0?? ?0.01?? ?2.462311558?? ?507?? ?0.00845
0.999001067?? ?9.99E-04?? ?9.99E-04?? ?9.99E-04?? ?1398?? ?0?? ?0.001?? ?12.98492462?? ?2584?? ?0.043066667
0.999900049?? ?1.00E-04?? ?9.99E-05?? ?9.99E-05?? ?10590?? ?0?? ?1.00E-04?? ?74.13065327?? ?14752?? ?0.245866667
0.999910044?? ?9.00E-05?? ?9.00E-05?? ?9.00E-05?? ?11673?? ?0?? ?9.00E-05?? ?77.95979899?? ?15529?? ?0.258816667
0.999920041?? ?8.00E-05?? ?8.00E-05?? ?8.00E-05?? ?13043?? ?0?? ?8.00E-05?? ?88.72361809?? ?17657?? ?0.294283333
0.999930029?? ?7.00E-05?? ?7.00E-05?? ?7.00E-05?? ?14783?? ?0?? ?7.00E-05?? ?100.798995?? ?20075?? ?0.334583333
0.999940023?? ?6.00E-05?? ?6.00E-05?? ?6.00E-05?? ?17128?? ?0?? ?6.00E-05?? ?115.5527638?? ?22995?? ?0.38325
0.999950023?? ?5.00E-05?? ?5.00E-05?? ?5.00E-05?? ?20378?? ?0?? ?5.00E-05?? ?141.6733668?? ?28193?? ?0.469883333
0.999960015?? ?4.00E-05?? ?4.00E-05?? ?4.00E-05?? ?25185?? ?0?? ?4.00E-05?? ?175.1155779?? ?34849?? ?0.580816667
0.999970011?? ?3.00E-05?? ?3.00E-05?? ?3.00E-05?? ?33209?? ?0?? ?3.00E-05?? ?230.638191?? ?45897?? ?0.76495
0.999980007?? ?2.00E-05?? ?2.00E-05?? ?2.00E-05?? ?49229?? ?0?? ?2.00E-05?? ?339.8190955?? ?67631?? ?1.127183333
0.999990004?? ?1.00E-05?? ?9.99E-06?? ?1.00E-05?? ?97192?? ?0?? ?1.00E-05?? ?671.6934673?? ?133680?? ?2.228
0.999991003?? ?9.00E-06?? ?9.00E-06?? ?9.00E-06?? ?107913?? ?0?? ?9.00E-06?? ?740.879397?? ?147437?? ?2.457283333
0.999992003?? ?8.00E-06?? ?8.00E-06?? ?8.00E-06?? ?121242?? ?0?? ?8.00E-06?? ?837.6030151?? ?166685?? ?2.778083333
0.999993002?? ?7.00E-06?? ?7.00E-06?? ?7.00E-06?? ?138604?? ?0?? ?7.00E-06?? ?956.9949749?? ?190442?? ?3.174033333
0.999994002?? ?6.00E-06?? ?6.00E-06?? ?6.00E-06?? ?161638?? ?0?? ?6.00E-06?? ?1119.080402?? ?222697?? ?3.711616667
0.999995002?? ?5.00E-06?? ?5.00E-06?? ?5.00E-06?? ?193006?? ?0?? ?5.00E-06?? ?1340.954774?? ?266862?? ?4.4477
0.999996001?? ?4.00E-06?? ?4.00E-06?? ?4.00E-06?? ?240321?? ?0?? ?4.00E-06?? ?1673.728643?? ?333072?? ?5.5512
0.999997001?? ?3.00E-06?? ?3.00E-06?? ?3.00E-06?? ?320678?? ?0?? ?3.00E-06?? ?2218.623116?? ?441506?? ?7.358433333
0.999998?? ?2.00E-06?? ?2.00E-06?? ?2.00E-06?? ?481138?? ?0?? ?2.00E-06?? ?3369.929648?? ?670625?? ?11.17708333
0.999999?? ?1.00E-06?? ?1.00E-06?? ?1.00E-06?? ?962243?? ?0?? ?1.00E-06?? ?6656.326633?? ?1324615?? ?22.07691667
0.9999991?? ?9.00E-07?? ?9.00E-07?? ?9.00E-07?? ?1068340?? ?0?? ?9.00E-07?? ?7402.738693?? ?1473150?? ?24.5525
0.9999992?? ?8.00E-07?? ?8.00E-07?? ?8.00E-07?? ?1202423?? ?0?? ?8.00E-07?? ?8283.81407?? ?1648481?? ?27.47468333
0.9999993?? ?7.00E-07?? ?7.00E-07?? ?7.00E-07?? ?1375220?? ?0?? ?7.00E-07?? ?9110.095477?? ?1812943?? ?30.21571667
0.9999994?? ?6.00E-07?? ?6.00E-07?? ?6.00E-07?? ?1601415?? ?0?? ?6.00E-07?? ?10525.52261?? ?2094580?? ?34.90966667
0.9999995?? ?5.00E-07?? ?5.00E-07?? ?5.00E-07?? ?1927719?? ?0?? ?5.00E-07?? ?12147.62312?? ?2417392?? ?40.28986667
0.9999996?? ?4.00E-07?? ?4.00E-07?? ?4.00E-07?? ?2410357?? ?0?? ?4.00E-07?? ?15609.35678?? ?3106310?? ?51.77183333
0.9999997?? ?3.00E-07?? ?3.00E-07?? ?3.00E-07?? ?3220585?? ?0?? ?3.00E-07?? ?20894.52261?? ?4158010?? ?69.30016667
0.9999998?? ?2.00E-07?? ?2.00E-07?? ?2.00E-07?? ?4829216?? ?0?? ?2.00E-07?? ?31439.65829?? ?6256492?? ?104.2748667
0.9999999?? ?1.00E-07?? ?1.00E-07?? ?1.00E-07?? ?9688361?? ?0?? ?1.00E-07?? ?64211.86432?? ?12778161?? ?212.96935
?? ??? ??? ??? ??? ??? ??? ??? ??? ?
?? ??? ??? ??? ??? ??? ??? ??? ??? ?665.2375

dr-2-10-2?? ??? ??? ??? ??? ??? ??? ?
?? ??? ??? ??? ??? ??? ??? ?
f2[0]?? ?f2[1]?? ?迭代次數n?? ?平均準確率p-ave?? ?δ?? ?耗時ms/次?? ?耗時ms/199次?? ?耗時 min/199
0.526584111?? ?0.470703963?? ?1.869346734?? ?0?? ?0.5?? ?0.472361809?? ?110?? ?0.001833333
0.61933022?? ?0.378788514?? ?5.316582915?? ?0?? ?0.4?? ?0.16080402?? ?47?? ?0.000783333
0.714022291?? ?0.285632622?? ?10.34673367?? ?0?? ?0.3?? ?0.08040201?? ?31?? ?0.000516667
0.806512411?? ?0.192506914?? ?18.5678392?? ?0?? ?0.2?? ?0.16080402?? ?47?? ?0.000783333
0.902097214?? ?0.097758283?? ?39.7839196?? ?0?? ?0.1?? ?0.236180905?? ?47?? ?0.000783333
0.99003051?? ?0.009968748?? ?326.8140704?? ?0?? ?0.01?? ?1.487437186?? ?312?? ?0.0052
0.999000471?? ?1.00E-03?? ?2555.768844?? ?0?? ?0.001?? ?9.201005025?? ?1831?? ?0.030516667
0.999900011?? ?1.00E-04?? ?21902.38693?? ?0?? ?1.00E-04?? ?62.85427136?? ?12508?? ?0.208466667
0.999910009?? ?9.00E-05?? ?23880.13568?? ?0?? ?9.00E-05?? ?63.48743719?? ?12634?? ?0.210566667
0.999920007?? ?8.00E-05?? ?27373.11558?? ?0?? ?8.00E-05?? ?73.70854271?? ?14669?? ?0.244483333
0.999930008?? ?7.00E-05?? ?30471.34171?? ?0?? ?7.00E-05?? ?82.66834171?? ?16466?? ?0.274433333
0.999940006?? ?6.00E-05?? ?33747.86935?? ?0?? ?6.00E-05?? ?89.8241206?? ?17875?? ?0.297916667
0.999950004?? ?5.00E-05?? ?41400.74372?? ?0?? ?5.00E-05?? ?111.1809045?? ?22141?? ?0.369016667
0.999960004?? ?4.00E-05?? ?50904.86935?? ?0?? ?4.00E-05?? ?139.040201?? ?27672?? ?0.4612
0.999970002?? ?3.00E-05?? ?68669.09045?? ?0?? ?3.00E-05?? ?184.2763819?? ?36671?? ?0.611183333
0.999980001?? ?2.00E-05?? ?98491.42211?? ?0?? ?2.00E-05?? ?266.6532663?? ?53096?? ?0.884933333
0.999990001?? ?1.00E-05?? ?193272.4774?? ?0?? ?1.00E-05?? ?524.718593?? ?104451?? ?1.74085
0.999991001?? ?9.00E-06?? ?206907.4774?? ?0?? ?9.00E-06?? ?558.1105528?? ?111064?? ?1.851066667
0.999992001?? ?8.00E-06?? ?237203.201?? ?0?? ?8.00E-06?? ?642.5778894?? ?127873?? ?2.131216667
0.999993?? ?7.00E-06?? ?269616.4372?? ?0?? ?7.00E-06?? ?727.0201005?? ?144692?? ?2.411533333
0.999994?? ?6.00E-06?? ?308451.3015?? ?0?? ?6.00E-06?? ?830.2512563?? ?165220?? ?2.753666667
0.999995?? ?5.00E-06?? ?366383.0905?? ?0?? ?5.00E-06?? ?987.0050251?? ?196414?? ?3.273566667
0.999996?? ?4.00E-06?? ?441795.6482?? ?0?? ?4.00E-06?? ?1193.38191?? ?237499?? ?3.958316667
0.999997?? ?3.00E-06?? ?587062.9196?? ?0?? ?3.00E-06?? ?1594.281407?? ?317262?? ?5.2877
0.999998?? ?2.00E-06?? ?881343.3819?? ?0?? ?2.00E-06?? ?2384.386935?? ?474493?? ?7.908216667
0.999999?? ?1.00E-06?? ?1691667.427?? ?0?? ?1.00E-06?? ?4240.211055?? ?843802?? ?14.06336667
0.9999991?? ?9.00E-07?? ?1861192.327?? ?0?? ?9.00E-07?? ?5102.648241?? ?1015444?? ?16.92406667
0.9999992?? ?8.00E-07?? ?2080246.372?? ?0?? ?8.00E-07?? ?5709.417085?? ?1136174?? ?18.93623333
0.9999993?? ?7.00E-07?? ?2464329.97?? ?0?? ?7.00E-07?? ?6725.005025?? ?1338292?? ?22.30486667
0.9999994?? ?6.00E-07?? ?2925796.92?? ?0?? ?6.00E-07?? ?7890.638191?? ?1570237?? ?26.17061667
0.9999995?? ?5.00E-07?? ?3289760.563?? ?0?? ?5.00E-07?? ?8888.251256?? ?1768762?? ?29.47936667
0.9999996?? ?4.00E-07?? ?4007427.271?? ?0?? ?4.00E-07?? ?10831.90955?? ?2155550?? ?35.92583333
0.9999997?? ?3.00E-07?? ?5245088.296?? ?0?? ?3.00E-07?? ?14310.68342?? ?2847826?? ?47.46376667
0.9999998?? ?2.00E-07?? ?7786895.241?? ?0?? ?2.00E-07?? ?21688.41709?? ?4316003?? ?71.93338333
0.9999999?? ?1.00E-07?? ?1.58E+07?? ?0?? ?1.00E-07?? ?42070.00503?? ?8371946?? ?139.5324333
?? ??? ??? ??? ??? ??? ??? ?
??? ? ?? ? ?? ? ?? ? ?? ? ?? ? ?? ?457.6526833

dr-3-10-3?? ??? ??? ??? ??? ??? ??? ??? ?
?? ??? ??? ??? ??? ??? ??? ??? ?
f2[0]?? ?f2[1]?? ?f2[2]?? ?迭代次數n?? ?平均準確率p-ave?? ?δ?? ?耗時ms/次?? ?耗時ms/199次?? ?耗時 min/199
0.531998108?? ?0.4668063?? ?0.467841829?? ?2.040201005?? ?0?? ?0.5?? ?0.391959799?? ?78?? ?0.0013
0.625916993?? ?0.374693698?? ?0.373937912?? ?5.577889447?? ?0?? ?0.4?? ?0.236180905?? ?78?? ?0.0013
0.717575379?? ?0.283496485?? ?0.282197138?? ?10.4321608?? ?0?? ?0.3?? ?0.155778894?? ?31?? ?0.000516667
0.809919228?? ?0.190881722?? ?0.190713318?? ?18.48241206?? ?0?? ?0.2?? ?0.16080402?? ?47?? ?0.000783333
0.902821459?? ?0.097322419?? ?0.09703025?? ?38.00502513?? ?0?? ?0.1?? ?0.236180905?? ?47?? ?0.000783333
0.990045238?? ?0.009953845?? ?0.009955177?? ?281.0904523?? ?0?? ?0.01?? ?1.567839196?? ?312?? ?0.0052
0.999000849?? ?9.99E-04?? ?9.99E-04?? ?1994.175879?? ?0?? ?0.001?? ?8.261306533?? ?1644?? ?0.0274
0.999900027?? ?1.00E-04?? ?1.00E-04?? ?16359.0402?? ?0?? ?1.00E-04?? ?55.13567839?? ?11020?? ?0.183666667
0.999910024?? ?9.00E-05?? ?9.00E-05?? ?18649.9598?? ?0?? ?9.00E-05?? ?56.92462312?? ?11344?? ?0.189066667
0.999920021?? ?8.00E-05?? ?8.00E-05?? ?20427.95477?? ?0?? ?8.00E-05?? ?60.57286432?? ?12069?? ?0.20115
0.999930016?? ?7.00E-05?? ?7.00E-05?? ?23575.8593?? ?0?? ?7.00E-05?? ?70.74874372?? ?14079?? ?0.23465
0.999940017?? ?6.00E-05?? ?6.00E-05?? ?26453.01508?? ?0?? ?6.00E-05?? ?79.9798995?? ?15931?? ?0.265516667
0.999950013?? ?5.00E-05?? ?5.00E-05?? ?31641.95477?? ?0?? ?5.00E-05?? ?96.27638191?? ?19159?? ?0.319316667
0.999960009?? ?4.00E-05?? ?4.00E-05?? ?38461.94975?? ?0?? ?4.00E-05?? ?116.080402?? ?23100?? ?0.385
0.999970008?? ?3.00E-05?? ?3.00E-05?? ?51233.01508?? ?0?? ?3.00E-05?? ?155.4924623?? ?30943?? ?0.515716667
0.999980005?? ?2.00E-05?? ?2.00E-05?? ?74906.1206?? ?0?? ?2.00E-05?? ?226.8291457?? ?45139?? ?0.752316667
0.999990003?? ?1.00E-05?? ?1.00E-05?? ?144249.7286?? ?0?? ?1.00E-05?? ?437.2713568?? ?87032?? ?1.450533333
0.999991002?? ?9.00E-06?? ?9.00E-06?? ?158850.8543?? ?0?? ?9.00E-06?? ?479.3115578?? ?95383?? ?1.589716667
0.999992002?? ?8.00E-06?? ?8.00E-06?? ?180718.5427?? ?0?? ?8.00E-06?? ?545.5276382?? ?108560?? ?1.809333333
0.999993002?? ?7.00E-06?? ?7.00E-06?? ?202380.0402?? ?0?? ?7.00E-06?? ?622.5527638?? ?123894?? ?2.0649
0.999994001?? ?6.00E-06?? ?6.00E-06?? ?240200?? ?0?? ?6.00E-06?? ?722.5929648?? ?143808?? ?2.3968
0.999995001?? ?5.00E-06?? ?5.00E-06?? ?279138.8844?? ?0?? ?5.00E-06?? ?839.4120603?? ?167050?? ?2.784166667
0.999996001?? ?4.00E-06?? ?4.00E-06?? ?344849.6935?? ?0?? ?4.00E-06?? ?1036.477387?? ?206264?? ?3.437733333
0.999997001?? ?3.00E-06?? ?3.00E-06?? ?459529.0302?? ?0?? ?3.00E-06?? ?1381.035176?? ?274831?? ?4.580516667
0.999998?? ?2.00E-06?? ?2.00E-06?? ?672539.7437?? ?0?? ?2.00E-06?? ?2023.020101?? ?402583?? ?6.709716667
0.999999?? ?1.00E-06?? ?1.00E-06?? ?1292568.874?? ?0?? ?1.00E-06?? ?3888.683417?? ?773855?? ?12.89758333
0.9999991?? ?9.00E-07?? ?9.00E-07?? ?1469246.196?? ?0?? ?9.00E-07?? ?4528.236181?? ?901121?? ?15.01868333
0.9999992?? ?8.00E-07?? ?8.00E-07?? ?1621744.271?? ?0?? ?8.00E-07?? ?5122.98995?? ?1019482?? ?16.99136667
0.9999993?? ?7.00E-07?? ?7.00E-07?? ?1797949.739?? ?0?? ?7.00E-07?? ?5819.79397?? ?1158147?? ?19.30245
0.9999994?? ?6.00E-07?? ?6.00E-07?? ?2091886.859?? ?0?? ?6.00E-07?? ?6800.984925?? ?1353401?? ?22.55668333
0.9999995?? ?5.00E-07?? ?5.00E-07?? ?2467525.844?? ?0?? ?5.00E-07?? ?8110.522613?? ?1613995?? ?26.89991667
0.9999996?? ?4.00E-07?? ?4.00E-07?? ?3216941.045?? ?0?? ?4.00E-07?? ?9953.045226?? ?1980658?? ?33.01096667
0.9999997?? ?3.00E-07?? ?3.00E-07?? ?4254122.518?? ?0?? ?3.00E-07?? ?12569.71859?? ?2501374?? ?41.68956667
0.9999998?? ?2.00E-07?? ?2.00E-07?? ?5986582.417?? ?0?? ?2.00E-07?? ?18259.92462?? ?3633725?? ?60.56208333
0.9999999?? ?1.00E-07?? ?1.00E-07?? ?1.23E+07?? ?0?? ?1.00E-07?? ?38868.19095?? ?7734773?? ?128.9128833
?? ??? ??? ??? ??? ??? ??? ??? ?
?? ??? ??? ??? ??? ??? ??? ??? ?407.7492833

dr-4-10-4?? ??? ??? ??? ??? ??? ??? ??? ??? ?
?? ??? ??? ??? ??? ??? ??? ??? ??? ?
f2[0]?? ?f2[1]?? ?f2[2]?? ?f2[3]?? ?迭代次數n?? ?平均準確率p-ave?? ?δ?? ?耗時ms/次?? ?耗時ms/199次?? ?耗時 min/199
0.535716861?? ?0.463529881?? ?0.462201609?? ?0.464102577?? ?3?? ?0?? ?0.5?? ?0.547738693?? ?109?? ?0.001816667
0.626382757?? ?0.370585994?? ?0.369941534?? ?0.372025326?? ?7?? ?0?? ?0.4?? ?0.221105528?? ?51?? ?0.00085
0.718509103?? ?0.281085664?? ?0.281285396?? ?0.281171568?? ?12?? ?0?? ?0.3?? ?0.135678392?? ?27?? ?0.00045
0.810595708?? ?0.189874827?? ?0.189742401?? ?0.188944534?? ?20?? ?0?? ?0.2?? ?0.180904523?? ?40?? ?0.000666667
0.903273004?? ?0.096510407?? ?0.096590552?? ?0.096482455?? ?42?? ?0?? ?0.1?? ?0.311557789?? ?62?? ?0.001033333
0.99006448?? ?0.009931742?? ?0.009934172?? ?0.009929011?? ?347?? ?0?? ?0.01?? ?1.768844221?? ?352?? ?0.005866667
0.999001338?? ?9.99E-04?? ?9.99E-04?? ?9.99E-04?? ?2942?? ?0?? ?0.001?? ?8.376884422?? ?1668?? ?0.0278
0.99990005?? ?1.00E-04?? ?9.99E-05?? ?9.99E-05?? ?23884?? ?0?? ?1.00E-04?? ?51.49748744?? ?10254?? ?0.1709
0.999910042?? ?9.00E-05?? ?9.00E-05?? ?9.00E-05?? ?37315?? ?0?? ?9.00E-05?? ?54.46231156?? ?10858?? ?0.180966667
0.999920041?? ?8.00E-05?? ?8.00E-05?? ?8.00E-05?? ?26333?? ?0?? ?8.00E-05?? ?60.04020101?? ?11964?? ?0.1994
0.99993003?? ?7.00E-05?? ?7.00E-05?? ?7.00E-05?? ?46405?? ?0?? ?7.00E-05?? ?67.23115578?? ?13387?? ?0.223116667
0.999940028?? ?6.00E-05?? ?6.00E-05?? ?6.00E-05?? ?43844?? ?0?? ?6.00E-05?? ?80.77386935?? ?16075?? ?0.267916667
0.999950023?? ?5.00E-05?? ?5.00E-05?? ?5.00E-05?? ?57098?? ?0?? ?5.00E-05?? ?94.61809045?? ?18837?? ?0.31395
0.999960017?? ?4.00E-05?? ?4.00E-05?? ?4.00E-05?? ?63706?? ?0?? ?4.00E-05?? ?117.3819095?? ?23359?? ?0.389316667
0.999970013?? ?3.00E-05?? ?3.00E-05?? ?3.00E-05?? ?102785?? ?0?? ?3.00E-05?? ?152.3819095?? ?30332?? ?0.505533333
0.999980009?? ?2.00E-05?? ?2.00E-05?? ?2.00E-05?? ?131495?? ?0?? ?2.00E-05?? ?223.3517588?? ?44456?? ?0.740933333
0.999990004?? ?1.00E-05?? ?1.00E-05?? ?1.00E-05?? ?203355?? ?0?? ?1.00E-05?? ?432.8542714?? ?86146?? ?1.435766667
0.999991003?? ?9.00E-06?? ?9.00E-06?? ?9.00E-06?? ?296829?? ?0?? ?9.00E-06?? ?485.1005025?? ?96535?? ?1.608916667
0.999992003?? ?8.00E-06?? ?8.00E-06?? ?8.00E-06?? ?363607?? ?0?? ?8.00E-06?? ?553.4522613?? ?110137?? ?1.835616667
0.999993002?? ?7.00E-06?? ?7.00E-06?? ?7.00E-06?? ?421455?? ?0?? ?7.00E-06?? ?624.6884422?? ?124313?? ?2.071883333
0.999994002?? ?6.00E-06?? ?6.00E-06?? ?6.00E-06?? ?405253?? ?0?? ?6.00E-06?? ?720.0854271?? ?143305?? ?2.388416667
0.999995002?? ?5.00E-06?? ?5.00E-06?? ?5.00E-06?? ?438198?? ?0?? ?5.00E-06?? ?846.7085427?? ?168511?? ?2.808516667
0.999996001?? ?4.00E-06?? ?4.00E-06?? ?4.00E-06?? ?733902?? ?0?? ?4.00E-06?? ?1062.241206?? ?211386?? ?3.5231
0.999997001?? ?3.00E-06?? ?3.00E-06?? ?3.00E-06?? ?747879?? ?0?? ?3.00E-06?? ?1375.070352?? ?273639?? ?4.56065
0.999998001?? ?2.00E-06?? ?2.00E-06?? ?2.00E-06?? ?1245116?? ?0?? ?2.00E-06?? ?2037.321608?? ?405442?? ?6.757366667
0.999999?? ?1.00E-06?? ?1.00E-06?? ?1.00E-06?? ?2046819?? ?0?? ?1.00E-06?? ?3900.668342?? ?776234?? ?12.93723333
0.9999991?? ?9.00E-07?? ?9.00E-07?? ?9.00E-07?? ?2010564?? ?0?? ?9.00E-07?? ?4260.743719?? ?847889?? ?14.13148333
0.9999992?? ?8.00E-07?? ?8.00E-07?? ?8.00E-07?? ?2166817?? ?0?? ?8.00E-07?? ?4823.21608?? ?959820?? ?15.997
0.9999993?? ?7.00E-07?? ?7.00E-07?? ?7.00E-07?? ?3092601?? ?0?? ?7.00E-07?? ?5536.517588?? ?1101799?? ?18.36331667
0.9999994?? ?6.00E-07?? ?6.00E-07?? ?6.00E-07?? ?2729326?? ?0?? ?6.00E-07?? ?6414.592965?? ?1276520?? ?21.27533333
0.9999995?? ?5.00E-07?? ?5.00E-07?? ?5.00E-07?? ?3698517?? ?0?? ?5.00E-07?? ?7769.160804?? ?1546067?? ?25.76778333
0.9999996?? ?4.00E-07?? ?4.00E-07?? ?4.00E-07?? ?5727957?? ?0?? ?4.00E-07?? ?9313.638191?? ?1853418?? ?30.8903
0.9999997?? ?3.00E-07?? ?3.00E-07?? ?3.00E-07?? ?6026219?? ?0?? ?3.00E-07?? ?12582.55276?? ?2503943?? ?41.73238333
0.9999998?? ?2.00E-07?? ?2.00E-07?? ?2.00E-07?? ?1.07E+07?? ?0?? ?2.00E-07?? ?18166.89447?? ?3615217?? ?60.25361667
0.9999999?? ?1.00E-07?? ?1.00E-07?? ?1.00E-07?? ?2.71E+07?? ?0?? ?1.00E-07?? ?36836.72864?? ?7330514?? ?122.1752333
?? ??? ??? ??? ??? ??? ??? ??? ??? ?
?? ??? ??? ??? ??? ??? ??? ??? ??? ?393.5444333

本次實驗原始數據比較多有感興趣的朋友可以在我的資源里下載

?

總結

以上是生活随笔為你收集整理的输入对5层网络迭代次数的影响的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。