Algorithm 1. ML-CNN algorithm.

Input: Training Data X = {xi,(yi × zi)}

Output: Network layer parameter W, LIL, LBCD

1 Given: minibatch n, learning rate α, μ and hyperparameter λ and λ1

2 Initialization: {t, W, θ, cj}

3 t = 1

4 while(t != T) {compute the aggregate loss Lagg = LBCE + λLIL

5 update LBCE

6 ${\gamma }^{t+1}={\gamma }^t-\mu (\mathrm{\partial }{L}_{BCE}^t)/(\mathrm{\partial }{\gamma }^t)$

7 update LIL

8 cjt + 1 − α Δcjt

9 update backpropagation error

10 $\mathrm{\partial }{L}^t/\mathrm{\partial }{x}_i^t=\mathrm{\partial }{L}_{BCE}^t/\mathrm{\partial }{x}_i^t+\lambda (\mathrm{\partial }{L}_{IL}^t/\mathrm{\partial }{x}_i^t)$

11 Update network layer parameter

12 $W^{t+1}=W^t-\mu \mathrm{\partial }{L}^t/\mathrm{\partial }{W}^t=W^t-\mu (\mathrm{\partial }{L}^t/\mathrm{\partial }{x}_i^t)(\mathrm{\partial }{x}_i^t/\mathrm{\partial }{W}^t)$

13 t = t + 1}