Table 4.

The outcomes of Bayesian-based tuned CNN in scenario 2.

Iter	Objective	depth	Learn rate	Momentum	L2Regularize	Iter	Objective	depth	Learn rate	Momentum	L2Regularize
1	0.17359	5	0.56005	0.89236	2.5293e-08	16	0.39151	1	0.85309	0.80126	0.0047163
2	0.086796	2	0.45045	0.9174	2.0423e-10	17	0.097876	1	0.048194	0.81467	1.1155e-10
3	0.19668	5	0.045905	0.86003	0.0030503	18	0.065559	1	0.015893	0.92199	3.0676e-07
4	0.09603	2	0.22518	0.85151	5.6765e-05	19	0.089566	1	0.20578	0.83616	1.0967e-10
5	0.66759	2	0.96209	0.97666	1.0098e-10	20	0.057248	1	0.055434	0.81781	6.2685e-06
6	0.0988	2	0.69253	0.82806	2.3652e-08	21	0.057248	2	0.010601	0.90455	4.7382e-06
7	0.051708	1	0.045746	0.89761	1.5974e-08	22	0.047091	1	0.060467	0.91755	2.3564e-10
8	0.064635	1	0.027362	0.80004	0.000474	23	0.090489	1	0.043747	0.8017	5.659e-07
9	0.051708	1	0.017239	0.90674	0.00070454	24	0.064635	1	0.029504	0.91162	1.0623e-06
10	0.17452	1	0.91842	0.87403	1.1245e-10	25	0.065559	1	0.010295	0.83171	0.0034166
11	0.11634	5	0.010243	0.80031	0.0015368	26	0.075716	1	0.010218	0.81136	0.00069518
12	0.045245	1	0.1452	0.90559	5.8502e-10	27	0.038781	1	0.042721	0.84845	5.3403e-07
13	0.073869	1	0.53902	0.8108	1.3185e-10	28	0.043398	1	0.010106	0.87598	0.00042163
14	0.057248	1	0.019588	0.90257	2.3604e-10	29	0.15605	4	0.25799	0.81222	1.2145e-10
15	0.3518	1	0.55331	0.90567	0.0022727	30	0.056325	1	0.065363	0.83903	1.1961e-05