# Research on a Convolution Kernel Initialization Method for Speeding Up the Convergence of CNN
## 3 Results and Discussion
### Result
In the model, the operation of \"two convolutions and one pooling\" was performed alternately, then the two fully connection layers were connected; finally, the output layer used the softmax classifier to divide the images into 10 categories. The activation function was Relu, the update of the weight parameter used the Adam algorithm, and the loss function was the cross-entropy. The input of the network was the image whose size was 32 \(\times\) 32 \(\times\) 3 in the training set. The specific network structure diagram is shown in Figure 8. The basic parameter value of the network is shown in Table 2.  
Figure 8: The CNN structure. Figure 7: The original image and the texture image. Batch_Size: The parameter was the number of images that were input into the network each time. Therefore, 128 images were sent into the input layer each time during the network training. Epoch_Number: The parameter meant the total iteration times. In the experiment, these values were 20, 30, and 50, respectively. Learning_Rate: The parameter was the learning rate. Its initial value was 0.01, and its attenuation factor was 0.1. During the network training, when the current iteration number of times was less than 40% of the total iteration number, its value was 0.01. When the current iteration number was between 40% and 80% of the total iteration number, its value was 0.001. When the current iteration number was greater than 80%, its value was 0.001. Log_Frequency: The parameter was 391. It meant that the value of loss function was output at the terminal every 391 training. 391 training represented one epoch, which meant the update times when all data in the training set had been used once. In the experiment, there were 50,000 images in the training set, and the \"Batch_Size\" was 128, so the \"Log_Frequency\" was 50,000/128 = 391. On the appropriate hardware platform, the appropriate model and the iteration number are selected, then the recognition rate of the CIFAR-10 data set can reach more than 95% [29]. However, the purpose of this experiment was not to obtain a better recognition rate for the CIFAR-10 dataset but to verify the effectiveness of the convolution kernel initialization method based on LBP and SAE. In the experiment, under the same conditions, the CNN used the Xavier initialization method, the He initialization method, and the initialization method based on LBP and SAE to initialize the convolution kernel of the first convolutional layer, and the iteration number was 20, 30, and 50. The training results are shown in Figure 9. The results of the recognition rate are shown in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Batch_Size** & **Epoch_Number** & **Learning_Rate** & **Log_Frequency** \\ \hline
128 & 20, 30, 50 & 0.01 & 50,000/128 = 391 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The basic network parameter settings. Figure 9: The comparison of CNN loss of three initialization methods with different iteration times. Table 3 shows one of the several CNN training results. Although the recognition rate of the CNN for the test data was not exactly the same after each training, the result of each training was that the method proposed in this paper had a higher recognition rate. In this result, as the iteration times increased, the recognition rates of the three initialization methods improved. However, when the iteration times were increased to an extent, the recognition rate of the network was basically unchanged. Because as the iteration times increased, the network model gradually converged. When the network iterated 20 times, using the Xavier initialization method, the recognition accuracy was 73.93%, and using the He initialization method, the recognition accuracy was 78.51%. Compared with the Xavier method, the He method increased the recognition rate of the network by 4.58%. Using the initialization method based on LBP and SAE that was proposed in this paper, the recognition accuracy was 81.59%.