Table 11 Training and validation performance per epochs for fully connected neural network.
From: Transfer learning with XAI for robust malware and IoT network security
Epoch | Accuracy | Loss | Val accuracy | Val loss |
---|---|---|---|---|
1 | 0.5840 | 0.7105 | 0.6870 | 0.5473 |
2 | 0.6877 | 0.6003 | 0.6849 | 0.5035 |
3 | 0.7147 | 0.5584 | 0.6890 | 0.4797 |
4 | 0.7236 | 0.5399 | 0.7362 | 0.4633 |
5 | 0.7357 | 0.5263 | 0.7338 | 0.4495 |
6 | 0.7454 | 0.5116 | 0.7186 | 0.4409 |
7 | 0.7486 | 0.5108 | 0.7181 | 0.4330 |
8 | 0.7522 | 0.5012 | 0.7180 | 0.4270 |
9 | 0.7534 | 0.4981 | 0.7184 | 0.4223 |
10 | 0.7544 | 0.4946 | 0.7184 | 0.4171 |
11 | 0.7567 | 0.4885 | 0.7188 | 0.4139 |
12 | 0.7566 | 0.4867 | 0.7192 | 0.4101 |
13 | 0.7566 | 0.4828 | 0.7196 | 0.4071 |
14 | 0.7594 | 0.4807 | 0.7198 | 0.4039 |
15 | 0.7623 | 0.4735 | 0.7204 | 0.4006 |
16 | 0.7631 | 0.4723 | 0.7211 | 0.3983 |
17 | 0.7605 | 0.4721 | 0.7218 | 0.3958 |
18 | 0.7645 | 0.4680 | 0.7221 | 0.3933 |
19 | 0.7644 | 0.4654 | 0.7225 | 0.3906 |
20 | 0.7669 | 0.4637 | 0.7234 | 0.3880 |
21 | 0.7625 | 0.4641 | 0.7238 | 0.3858 |
22 | 0.7623 | 0.4658 | 0.7241 | 0.3842 |
23 | 0.7667 | 0.4597 | 0.7250 | 0.3820 |
24 | 0.7648 | 0.4585 | 0.7265 | 0.3801 |
25 | 0.7664 | 0.4533 | 0.7291 | 0.3783 |
26 | 0.7664 | 0.4593 | 0.7320 | 0.3762 |
27 | 0.7656 | 0.4530 | 0.7338 | 0.3753 |
28 | 0.7706 | 0.4456 | 0.7428 | 0.3729 |
29 | 0.7695 | 0.4485 | 0.7433 | 0.3715 |
30 | 0.7667 | 0.4510 | 0.7432 | 0.3686 |