## 4 Convolutional Neural Network
### Regression CNN
#### 4.2.2 Proposed Structure  
The proposed regression CNN model in the present study comprises a total of 23 layers: layers 1 to 16 are dedicated to feature extraction, while layers 17 to 23 are focused on regression. Layer 23 is the output layer, which includes four components representing the predicted \(r_{p}\), \(d_{p}/t\), \(h\), and \(\phi\) values, respectively. The detailed CNN regression model layer information is presented in Table 4. It is noted that, in addition to the dropout layers introduced in Section 4.1.2, several batch normalization layers are incorporated within the typical CNN layers to prevent overfitting [46; 47; 48]. As shown in Table 4, the tuned hyperparameters in the feature extraction process of the regression model, such as the filter size of the convolution layer, the pooling size, and the dropout rate, are the same as those in the classification CNN model, while the employed dropout rate in the regression process of the regression CNN model is 0.5. Figure 10 depicts the architecture of the developed regression model with the dimensions of different layers. Note that the dropout and batch normalization layers are excluded from the diagram as they do not impact the data dimensionality. The color scheme utilized to distinguish various layers in Figure 10 is consistent with those utilized in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Part** & **Layer No.** & **Layer Name** & **Parameters** \\ \hline \multirow{8}{*}{Feature extraction} & 1 & Convolution & 64 filters with size equals to (5 \(\times\) 5) \\ \cline{2-3}  & 2 & Batch normalization \\ \cline{2-3}  & 3 & Maxpooling & Pooling size equals to (2 \(\times\) 2) \\ \cline{2-3}  & 4 & Dropout & Rate = 0.2 \\ \cline{2-3}  & 5 & Convolution & 128 filters with size equals to (5 \(\times\) 5) \\ \cline{2-3}  & 6 & Batch normalization \\ \cline{2-3}  & 7 & Maxpooling & Pooling size equals to (2 \(\times\) 2) \\ \cline{2-3}  & 8 & Dropout & Rate = 0.2 \\ \cline{2-3}  & 9 & Convolution & 256 filters with size equals to (5 \(\times\) 5) \\ \cline{2-3}  & 10 & Batch normalization \\ \cline{2-3}  & 11 & Maxpooling & Pooling size equals to (2 \(\times\) 2) \\ \cline{2-3}  & 12 & Dropout & Rate = 0.2 \\ \cline{2-3}  & 13 & Convolution & 512 filters with size equals to (5 \(\times\) 5) \\ \cline{2-3}  & 14 & Batch normalization \\ \cline{2-3}  & 15 & Maxpooling & Pooling size equals to (2 \(\times\) 2) \\ \cline{2-3}  & 16 & Dropout & Rate = 0.2 \\ \hline \multirow{8}{*}{Regression} & 17 & Flatten layer & Unit number equals to 51,200 \\ \cline{2-3}  & 18 & Dropout & Rate = 0.5 \\ \cline{2-3}  & 19 & Fully connected layer & Unit number equals to 64 \\ \cline{2-3}  & 20 & Fully connected layer & Unit number equals to 128 \\ \cline{2-3}  & 21 & Fully connected layer & Unit number equals to 256 \\ \cline{2-3}  & 22 & Fully connected layer & Unit number equals to 512 \\ \cline{2-3}  & & Output 1 & Unit number equals to 1 \\ \cline{2-3}  & & Output 2 & Unit number equals to 1 \\ \cline{2-3}  & & Output 3 & Unit number equals to 1 \\ \cline{2-3}  & & Output 4 & Unit number equals to 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: CNN regression model layer information.