Table 5.

The outcomes of Bayesian-based tuned CNN in scenario-3.

Iter	Objective	Depth	Learn rate	Momentum	L2 Regularize	Iter	Objective	Depth	Learn rate	Momentum	L2 Regularize
1	0.073869	5	0.56005	0.89236	2.5293e-08	16	0.031394	1	0.3408	0.80021	2.085e-08
2	0.084026	2	0.45045	0.9174	2.0423e-10	17	0.061865	1	0.14746	0.80107	1.7148e-09
3	0.14681	5	0.045905	0.86003	0.0030503	18	0.1265	2	0.20049	0.97991	6.7533e-09
4	0.079409	2	0.22518	0.85151	5.6765e-05	19	0.34441	5	0.71311	0.97992	2.8196e-09
5	0.10249	4	0.57092	0.80002	5.0694e-05	20	0.065559	1	0.36462	0.80086	4.6695e-08
6	0.17359	5	0.60652	0.88909	2.5798e-08	21	0.13758	3	0.93362	0.84857	5.843e-09
7	0.078486	2	0.27832	0.87092	2.719e-07	22	0.056325	1	0.010313	0.84758	3.5044e-07
8	0.084026	2	0.64306	0.81325	5.4379e-09	23	0.054478	1	0.056957	0.92869	4.5579e-09
9	0.064635	3	0.020184	0.90189	1.1136e-10	24	0.048015	1	0.086999	0.81024	1.8238e-06
10	0.073869	1	0.99879	0.85691	3.9481e-10	25	0.056325	1	0.066059	0.97703	1.1357e-10
11	0.087719	3	0.082862	0.97899	1.7582e-07	26	0.028624	1	0.0104	0.80281	1.7329e-08
12	0.1145	1	0.30907	0.96899	4.8241e-09	27	0.058172	1	0.010883	0.80102	8.7027e-09
13	0.068329	3	0.022298	0.80173	1.6709e-08	28	0.037858	1	0.010189	0.88753	1.5256e-06
14	0.033241	3	0.016754	0.80022	2.0167e-06	29	0.2096	5	0.92303	0.80051	2.6785e-07
15	0.63343	4	0.39236	0.97969	4.2755e-06	30	0.058172	1	0.011612	0.97707	1.0257e-07