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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
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