# Research on Generalized Hybrid Probability Convolutional Neural Network
## 5 Experiment and Results  
### Research on the GHP-CNN's Classification Capabilities after Category Reduction  
Through the analysis of the CFM's generalization ability in the second part, the more source dataset categories used for training the CFM, the better the generalization ability. In the experiment, we used the CIFAR-100 and Caltech-256 datasets and the VGG and ResNet networks. After fully training VGG-16 with the CIFAR-100 dataset, the average accuracy of the 100-class test was 69.58%, and the fully trained convolutional layer was used as the CFM. Four, six, eight, twelve and sixteen subcategories were randomly selected from the CIFAR-100 data, and the corresponding classification accuracy of the subclasses was obtained after training with VGG-16. The features of these subclasses were extracted by the CFM and trained and tested with hybrid networks such as VGG-16-E, CNN-PNN, CNN-SVM, CNN-ELM and GHP-CNN. VGG-16-E is the result of training the MLP using the CFM in VGG-16. The results are shown in Table 2. After fully training ResNet-152 with Caltech-256, ResNet-152-E was the result of training the MLP using the CFM in ResNet-152. The results are shown in Table 3.  
In Table 2, when \(s=100\), the average classification accuracy of the corresponding 100 classes was determined, but for each different subclass \(t\), the corresponding classification accuracy was different. When the number of source data categories was determined (\(s=100\)), the corresponding CFMs could extract different categories of target features (\(t=4,6,8,12,16\)) and have significantly improved classification effects in different classifiers. Overall, the CFM trained by a multiclass dataset could extract features with a high separability and had a good generalization ability of feature mapping. When subclasses were reclassified, the results of the comparison algorithm were significantly higher than the original accuracy. Our GHP-CNN method had the highest classification accuracy. In Table 3, when \(s=256\), the average classification accuracy of the corresponding 256 classes was determined. When subclasses were reclassified, the results of the comparison algorithm were significantly higher than the original accuracy. Our GHP-CNN method had the highest classification accuracy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\mathbf{s=100}\) & \(\mathbf{t=4}\) & \(\mathbf{t=6}\) & \(\mathbf{t=8}\) & \(\mathbf{t=12}\) & \(\mathbf{t=16}\) \\ \hline VGG-16-E & 90.00 & 87.50 & 85.86 & 82.58 & 81.01 \\ \hline CNN-PNN & 94.0 & 90.67 & 89.00 & 85.25 & 82.63 \\ \hline CNN-SVM & 94.75 & 89.33 & 88.75 & 83.67 & 83.00 \\ \hline CNN-ELM & 95.00 & 90.83 & 89.50 & 85.75 & 83.01 \\ \hline GHP-CNN & 98.50 & 96.50 & 94.38 & 92.75 & 91.13 \\ \hline \end{tabular}
\end{table}
Table 2: Classification accuracy corresponding to different algorithms after category reduction with the CIFAR-100 dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\mathbf{s=256}\) & \(\mathbf{t=4}\) & \(\mathbf{t=6}\) & \(\mathbf{t=8}\) & \(\mathbf{t=12}\) & \(\mathbf{t=16}\) \\ \hline ResNet-152-E & 89.60 & 87.30 & 80.41 & 78.54 & 75.81 \\ \hline CNN-PNN & 89.03 & 82.35 & 79.84 & 75.61 & 72.34 \\ \hline CNN-SVM & 90.00 & 83.53 & 80.72 & 79.18 & 77.50 \\ \hline CNN-ELM & 82.50 & 73.37 & 72.83 & 71.62 & 70.63 \\ \hline GHP-CNN & 95.00 & 88.51 & 85.37 & 81.42 & 78.72 \\ \hline \end{tabular}
\end{table}
Table 3: Classification accuracy corresponding to different algorithms after category reduction with the Caltech-256 dataset.