## 5 Results and Discussion
### Results of Model Optimization and Training
#### 5.3.1 Results of Training Improved DenseNet  
The experimental results indicate that the model proposed in this paper outperforms the four model architectures of [PERSON] (2018) in terms of accuracy, whether with 6 questions or 12 questions. Improved DenseNet achieved an accuracy of 95.13% for 6-question graphics and 89.84% for 12-question graphics. Specifically, the model demonstrated better accuracy across multiple evaluation standards, whether in small-scale tests (6 questions) or larger-scale tests (12 questions). In contrast, the four model architectures in the most relevant literature showed slightly inferior accuracy for the same number of questions. This indicates that the model proposed in this paper has stronger stability and adaptability when handling different numbers of questions. Particularly when the number of questions increased to 12, the model in this paper still maintained high accuracy, whereas the accuracy of models in the relevant literature significantly declined. These results provide strong evidence for the superiority of the model proposed in this paper, especially in the application value in multi-question testing scenarios. The model demonstrates efficient performance under different testing conditions through effective feature extraction and classification methods, supporting its practical application. Table 5 shows the results of training improved DenseNet classification. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Channel 1** & **Channel 2** & **Channel 3** & **Accuracy** \\ \hline Zero array & Sketch & Question & 69\% \\ Sketch & Sketch & Question & 69\% \\ Zero array & Sketch & Question no. & 75\% \\
**Sketch** & **Sketch** & **Question no.** & **93\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of optimizing input channel data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Epoch** & **Training Loss** & **Validation Loss** & **Validation Accuracy (\%)** & **Testing Loss** & **Test Accuracy (\%)** \\ \hline ResNet50 & 49 & 0.0144 & 0.9082 & 82 & 1.2796 & 72 \\ ResNet101 & 18 & 0.0424 & 0.4895 & 79 & 0.63 & 73 \\ DenseNet201 & 15 & 0.0257 & 0.3794 & **86** & 0.2442 & **89** \\ InceptionResNetV2 & 9 & 0.0403 & 0.6244 & 85 & 0.5755 & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Pre-trained model experiment results for question 13. After training with 12-question graphics, to further enhance the model's adaptability to the task, fine-tuning with unfreezing layers was conducted. During the process of adjusting the number of frozen layers for improved DenseNet, it was found that when the number of frozen layers reached 90%, the model's accuracy significantly improved and performed excellently. Specifically, as the number of frozen layers decreased, the number of epochs required to achieve accuracy exceeding 90% was significantly reduced, indicating that the model could adapt to new data more quickly and training efficiency was greatly improved. However, as the number of frozen layers was further reduced, although training time was significantly shortened, it also led to a decrease in model accuracy. This is because unfreezing more layers makes the model parameters more flexible but also more susceptible to noise and specifics in the training data, thereby reducing the model's generalization capability. In the experiment of unfreezing layers, it was found that while unfreezing improved model performance, the improvement was not significant. It is hypothesized that this is due to DenseNet201 being pre-trained on the ImageNet dataset. The ImageNet dataset contains over 20,000 categories, such as \"cat\", \"dog\", \"balloon\", or \"strawberry\", each with hundreds of color images that have high complexity and diversity. Therefore, when transferring these pre-trained models to the black-and-white geometric images studied in this paper, excessive training may lead to overfitting due to the significant differences in features and complexity compared to ImageNet images.