Algorithm 1 Model training process by using multi-task adaptive dynamic adjustment loss function

Preparation:

Input: Training samples $D={\{(x_i,y_i^r,y_i^c)\}}_{i=1}^n$

Initial learning rate: γ

Training:

for j = 1, 2, …, N do:

if j = 1:

Input x into the model

Output:  ${\stackrel{⌢}{y}}_i^r$ $, {\widehat{y}}_i^c$

Calculate the loss function Equations (16) and (17)

break

else:

Input x into the model

Output:  ${\stackrel{⌢}{y}}_i^r$ $, {\widehat{y}}_i^c$

Calculate the αj and βj by Equations (19) and (20)

Calculate the regression and classification loss by Equations (16) and (17)

Calculate the total loss by Equation (18)

Update the model parameters W b by using Adam optimizer

End for

Return: The trained model parameters: W b