## 7 Experiments
### Dataset Splitting at a 7:3 Ratio
#### 7.3.2 Comparison of Loss and Accuracy in The Testing Stage  
Because the parameters in the training stage varied according to batch size, the average loss calculated in the training stage was not completely reflected in the testing stage. Therefore, NN performance could not be determined until the testing stage. In addition, the data obtained in the testing stage and training stage differed. Table 7 presents the data obtained in the testing stage.  
According to Table 7, expanding the network width such as in model WRN_k8 did not increase the representation in this experiment. Longer training might result in a more favorable outcome; however, this approach was inefficient. Moreover, the results of model WRN_b256 revealed sufficient representation with the parameters set at N = 2 and k = 4. The following experiments revealed that with sufficient training time, the model with a batch size of 128 could have similar performance to one with a batch size of 256. This observation verified that a larger batch size benefited the stability of the system.  
With respect to the experiment results of model WRN_b256, which was excellent in terms of NN performance, the results obtained from the testing set were notably different  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Network** & **Loss** & **Accuracy** \\ \hline Training Set & WRN\_k4 & 1.667588 & 66.7036\% \\ \hline  & WRN\_k8 & 1.326531 & 66.7562\% \\ \hline  & WRN\_b256 & 0.738129 & 91.3373\% \\ \hline  & WRN\_kernel5 & 0.363532 & 88.5657\% \\ \hline  & WRN\_ex3D & 0.224468 & 93.8139\% \\ \hline Testing Set & WRN\_k4 & 1.870392 & 58.6575\% \\ \hline  & WRN\_k8 & 1.554162 & 59.6236\% \\ \hline  & WRN\_b256 & 0.963743 & 82.5471\% \\ \hline  & WRN\_kernel5 & 0.783152 & 78.2560\% \\ \hline  & WRN\_ex3D & 0.304827 & 90.3513\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Testing results under the condition where the samples were randomly distributed between training and testing sets at a 7-3 ratio.  
Figure 11: Terminal convergence results of average loss under the condition where the samples were randomly distributed between training and testing sets at a 7-3 ratio (epochs 201–369).  
from those obtained from the training set. Two possible reasons for this outcome were overfitting and the need to improve the representation of the features that the NN had learned from the training set. Because numerous anti-overfitting methods were employed, such as batch normalization and dropout, the second possibility was more likely to be true. Therefore, this study intended to improve the NN structure to increase the number of features learned.  
After referencing [4], the present study incorporated partial 3D convolution layers to extract local temporal features and obtained model WRN_ex3D. The results revealed that this approach effectively improved the convergence rate and reduced the difference between the training and testing set results, with only 864 additional parameters required. Although further computation and more graphics card memory were required to store matrices generated by 3D convolution, this model was more efficient than the other models.