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Chazhoor et al. Intell Robot 2022;2:1-19 https://dx.doi.org/10.20517/ir.2021.15 Page 11

Table 3. The mean training and validation accuracies and losses for AlexNet architecture for 20 epochs
Mean_AlexNet
Epoch
Training accuracy Validation accuracy Training loss Validation loss
1 0.5815 0.57302 1.00228 1.1308
2 0.6675 0.64806 0.80658 1.09448
3 0.7177 0.5804 0.69244 1.1246
4 0.73384 0.64656 0.6721 1.01474
5 0.77882 0.67598 0.55144 0.9506
6 0.78652 0.66568 0.51194 1.04706
7 0.79548 0.7093 0.50188 0.84044
8 0.84654 0.7696 0.36054 0.82302
9 0.87302 0.7642 0.30162 0.89168
10 0.87962 0.77646 0.28896 0.90384
11 0.87458 0.77746 0.29108 0.92258
12 0.88206 0.78874 0.28282 0.8886
13 0.88462 0.78236 0.26542 0.99196
14 0.88192 0.78532 0.26406 0.99434
15 0.89248 0.78972 0.25636 0.98168
16 0.89126 0.78972 0.2576 0.98266
17 0.88914 0.79118 0.25864 0.95596
18 0.897 0.79608 0.24166 0.95004
19 0.89344 0.79706 0.24634 0.9735
20 0.89602 0.79414 0.24826 0.98582

Table 4. The mean training and validation accuracies and losses for SqueezeNet architecture for 20 epochs
Mean SqueezeNet
Epoch
Training accuracy Validation accuracy Training loss Validation loss
1 0.47992 0.7281 1.02608 1.32476
2 0.64688 0.7437 0.78012 0.96076
3 0.7134 0.718 0.68612 1.05972
4 0.74428 0.67796 0.6426 1.14184
5 0.76116 0.7003 0.5903 0.81164
6 0.79006 0.70916 0.53186 0.88014
7 0.81026 0.65862 0.51222 0.89182
8 0.85586 0.69658 0.42766 0.81594
9 0.87364 0.70138 0.3871 0.89832
10 0.87874 0.70724 0.37834 0.99886
11 0.88684 0.6838 0.3752 0.9401
12 0.89062 0.69988 0.36256 0.93402
13 0.89798 0.69218 0.3465 0.94986
14 0.88878 0.7183 0.36842 0.8951
15 0.89504 0.70776 0.35906 0.97796
16 0.89798 0.70376 0.35146 1.0066
17 0.89896 0.70712 0.35242 0.99574
18 0.90166 0.70396 0.34732 1.00284
19 0.90422 0.70202 0.34508 1.01182
20 0.90238 0.70606 0.34562 0.9707