## 3 Experiments and Results
### Model Parameter Adjustment  
To analyze how different hyperparameters affect the model's performance, in this study, we employed the controlled variable method and conducted multiple rounds of experimental comparisons by setting different model parameters based on empirical tuning. That is, only one hyperparameter is changed each time, while all others are left unchanged, to accurately determine the given impact of a single variable on the model. Four different models were selected for this experiment, the results of which are presented in Figure 5.  
The stochastic gradient descent (SGD) can be used to iteratively update the model's parameters, but it is evidently prone to instability in the convergence process, with large fluctuations in its curve. The average stochastic gradient descent (ASGD) improves upon the SGD, by introducing the idea of parameter averaging, which also enhances convergence stability. Adaptive Moment Estimation (Adam) can adaptively adjust the learning rate and combines the advantages of the AdaGrad and RMSProp algorithms [33]. Compared with SGD, it converges faster and has an improved stability, but it is prone to the problem of decay. AdamW is an improvement to the Adam algorithm and is mainly used to solve the latter's occasional weight decay problem [34]. In comparing the experimental results, we found that AdamW had the best convergence speed and stability. A comparative experiment was then performed, by setting different parameter values for the learning rate and batch size. The learning rates were set to 1 \(\times\) 10\({}^{-3}\), 1 \(\times\) 10\({}^{-4}\), or 1 \(\times\) 10\({}^{-5}\), respectively, and the batch sizes to 16, 32, or 64, in combination. These results are shown in Figure 6.  
Figure 5: Model training loss curves for several different optimization algorithms. The batch size plays a crucial role in model training, because it determines the number of samples inputted to the model in each time step. If the batch size is set too large, the model is quite likely to fall into the dilemma of local optimality during training, consequently affecting its performance. Conversely, when the batch size is set too small, while this helps the model escape from local optimality, the training efficiency is significantly reduced, thereby incurring more time costs. The learning rate governs the step size of model parameter updates. If that value is set too high, the model is apt to skip the optimal solution due to overly large step sizes when updating parameters, and ultimately fails to converge; however, if set too small, the magnitude of each parameter update of the model could be minimal, greatly prolonging the training time required. Therefore, given the results (Figure 6), the best combination of parameters to use was a batch size of 32 and a learning rate of \(1\times 10^{-4}\). Similarly, another experiment was carried out, in which different parameter values were tested for the size of the convolutional kernel and the number of convolutional kernels. The former was set to \(2\times 2\), \(3\times 3\), or \(4\times 4\) and the latter to 32, 64, or 128, in combination. These results are shown in Figure 7.  
Figure 6: Results for the accuracy of different combinations of learning rates and batch sizes. Figure 7: Results for the accuracy of different combinations of convolution kernel sizes and numbers. This revealed a certain association between the size of the convolutional kernel and the accuracy of the model. As the former gradually increases, the latter does not change linearly. Instead, it first rose, reached a certain peak, and then fell. In the initial stage, a larger convolutional kernel could capture more abundant feature information. Nevertheless, when the size of the convolutional kernel exceeds a reasonable range, the feature extraction efficiency of the model will decrease, so it is likely to miss local key features, increasing the risk of overfitting and causing its accuracy to decline.