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