# CASVM: An Efficient Deep Learning Image Classification Method Combined with SVM
## 4 Experimental Results and Analysis
### Analysis of Experimental Results  
In Section 4.1, the results of the four datasets Anima10, Cifar10, Cifar100, and Fashion-MNIST are compared with the four network models CNN1, AlexNet, CNN2, and ResNet18 combined crosswise between different parameters. The final classification accuracies of the SVM and softmax classifiers are used as benchmarks to compare their effects on the four datasets with different parameter choices. From the results, it is seen that the loss function of the SVM classifier shows some improvement in network classification performance; a lower learning rate of 0.00001 can make the model accuracy smoother; and the Adam optimizer can further improve model accuracy. Therefore, we used the Adam optimizer and a learning rate of 0.00001 as the main parameters of CASVM. Figure 7 compares the image features in CA layers at different locations. Figure 7 shows the feature output plots of CASVM combined with CNN1. Figure 7a-d show the feature plots obtained from one image randomly selected from Animal10, Fashion-MNIST, Cifar10, and Cifar100, respectively, after training. Both the convolutional and CA layers of the model contain 64 channels, and the figure shows the first convolutional layer. By comparing the output image features of the CA layer at different positions, it is found that the characteristics of channels differ. There are great differences in the roles of different convolution kernels in CNN structures. Figure 7a shows that the CA layer is located after the first convolutional layer of the network, and it can be observed that the feature difference is not obvious. From this, it can be concluded that when the CA layer is located after the first convolutional layer in a shallow network like CNN1, the output features are still more detailed, and they contain detailed information about the underlying visual features. The third and fourth rows of the feature map in Figure 7a contain some uncorrelated features, which are not critical to image classification, so the CA layer should not be located after the first convolutional layer. As shown in Figure 7b,c, locating the CA layer in the middle of the network enhances the representation of edge contour, global, and texture information. As shown in Figure 7d, a CA layer close to the top layer can weaken invalid features and strengthen effective features, which can improve classification accuracy. Based on the CNN1 model, CA was introduced to three positions (bottom, middle, and top), and the Animal10 dataset obtained classification accuracies of 67%, 78%, and 80%, respectively. Also, ResNet18 was used to validate it, and the best result of 92% was obtained by embedding CA in the top layer of the residual block. For this purpose, the CA introduction position is set close to the top layer of the network model, except for the network model, which is very shallow. The most intuitive measure of a model's image classification accuracy is its classification accuracy versus loss value. We performed eight sets of comparison experiments to verify the advantages of CASVM in image classification, with results as shown in Tables 4 and 5, with four basic frameworks: model structure not embedded in CA and based on SVM classification, model structure not embedded in CA and based on softmax classification, model structure embedded in CA and based on SVM classification (CASVM), and model structure embedded in CA and based on softmax classification. The above four frameworks are compared to verify the classification accuracy of CASVM in four different CNN models based on CNN1, AlexNet, CNN2, and ResNet18. To verify the generalization ability of CASVM, the Fashion-MNIST, Cifar10, Anima10, and Cifar100 datasets were selected for training and testing, and images of the Animal10 dataset were preprocessed. Tables 4 and 5 show the results. Softmax is the network model for classification, with cross-entropy loss functions.