## 5 Experiment and Analysis
### Computation and Results Analysis  
#### 5.2.1 The Convolutional Neural Network  
(1) CNN parameters selection  
Concerning the CNN configuration, it is still an open question what hyper-parameters (e.g., number of layers, learning rate, size of the filters, batch-size, etc.) are useful to a greater or lesser extent for this task [42]. The hyper-parameters are adjusted in order to study the performance of the built CNN. The choice of learning rate and batch-size severely affects the training and testing results, so we adjust and study the learning rate and batch-size in this paper [20]. The structure of CNN is shown in Figure 3, and the configurations of each layer are listed in Table 1. In addition, before each round of training, the dataset is randomly disturbed, the network parameters are randomly initialized, the \(L_{2}\) regularization term is added to the fully connected layer F1, and a stochastic gradient descent (SGD) algorithm is used to train the network [35]. Before the training and testing of the BTRs dataset, 70% of the total sample is selected randomly as the training sample, and the remaining 30% is used as the testing sample (i.e., 3150 samples are selected as the training dataset and 1350 samples are used as the testing dataset). After training and testing, we mainly use Equation (9) (accuracy) to evaluate the performance of the CNN. (a) Learning rate  
An ideal learning rate will accelerate the convergence of the model, while an undesirable learning rate will even directly cause the loss of the objective function to explode and fail to complete the training [43]. In this section, the network iteration is set to 40 epochs, and the initial batch-size is set to 5. The training loss, training accuracy, testing loss and testing accuracy under different learning rates are shown in Table 5.  
From the data in Table 5, the training and testing curves are made as shown in Figure 8. Table 5 and Figure 8 show that both the training accuracy and testing accuracy are 100% around the learning rate of 0.01, and that the accuracy is highest and stable at this rate. During training and testing, the accuracy and loss of testing are basically consistent with the training accuracy and loss, indicating that there is no significant noise in the dataset, and the network performance is good. With the increase in the learning rate, the training accuracy and the test accuracy first increase, then remain stable, and finally reduce quickly (training loss and testing loss decrease at first, then keep stable, finally increase and keep stable), indicating that smaller and larger learning rates reduce the accuracy of the network. Therefore, in this experiment, the optimized learning rate is set to 0.01. 2. Batch-size  
When the SGD method is adopted, the batch-size has a great influence on network performance. In this section, we set the network iteration to 40 epochs, and the learning rate to 0.01. The training loss, training accuracy, testing loss, testing accuracy, and time cost of different batch-sizes are shown in Table 6.  
The training and testing curves are made as shown in Figure 9, according to the data in Table 6. From Table 6 and Figure 9, it is known that when the batch-sizes are 1, 3, and 5, the training accuracy and testing accuracy are both 100%, and that the accuracy is the highest and stable. With the increase in the batch-size, the training accuracy and testing accuracy remain stable at first, then reduce quickly (training loss and testing loss keep stable at first, then increase fast), indicating that larger batch-sizes reduce the accuracy of the network. It is also found that larger batch-sizes led to less time being consumed for each iteration. If we use graphics processing units (GPUs) to accelerate the computation process via parallel computation, we can significantly reduce the iteration time. Therefore, in this experiment, when the learning rate of the CNN model is set to 0.01 and the batch-size is set to 5, the  
Figure 8: Training and testing results under different learning rates.