# Research on a Convolution Kernel Initialization Method for Speeding Up the Convergence of CNN
## 3 Results and Discussion
### Discussion  
The size of the image is 32 \(\times\) 32 in the CIFAR-10 dataset. The target that is recognized in most images occupies a large portion of the image. The image was cut to several blocks after the LBP algorithm was completed according to the initialization method proposed in this paper. The size of the block was 3 \(\times\) 3, then the probability that the block contains the entire target to be recognized is small. Therefore, this method has a small improvement in the recognition accuracy of the CIFAR-10.  
Ten images were randomly selected from each category of typical images that were selected from the CIFAR-10 dataset, and the 10 images that represented all images of a certain category were performed using the initialization method proposed in this paper. Therefore, the initial value of the convolution kernel matched with the parts of some images rather than all images in the training dataset. In addition, the first layer convolution kernels of the CNN were initialized according to the method. The convolution kernels of the other layers still used the default initialization method in the TensorFlow framework. The training of convolution kernels of other layers still took plenty of time to make the network converge. As a result, based on the above two reasons, although this method can accelerate the convergence speed of a CNN, the effect is not very obvious.  
Compared with the Xavier initialization method and the He initialization method, the method proposed in this paper is relatively cumbersome to obtain the initial value of the convolution kernel. Firstly, the typical images were selected, then the texture images were extracted from the typical images, and the sparse autoencoder was trained to obtain the initial value of the convolution kernel. On the contrary, the Xavier initialization method  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Recognition Rate**} \\ \cline{2-4}  & **Epoch\_Num = 20** & **Epoch\_Num = 30** & **Epoch\_Num = 50** \\ \hline Xavier & 73.93\% & 74.9\% & 78.01\% \\ He & 78.51\% & 78.79\% & 80.01\% \\ LBP and SAE & 81.59\% & 81.81\% & 82.01\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison of the recognition rate of three initialization methods.  
and the He initialization method, which are relatively mature initialization methods, can be completed by directly using corresponding functions in the TensorFlow framework [30; 31; 32; 33; 34; 35; 36].  
As can be seen from the above experimental results and analysis, although the convolution kernel initialization method based on LBP and SAE proposed in this paper has many shortcomings, it still accelerates the convergence speed of the network and improves the recognition accuracy to an extent. The main reason is that the texture features of the image are extracted, which are processed by the sparse autoencoder as the initial value of the convolution kernel so that the convolution kernels match the local pattern of some images in the training samples. Therefore, it shows that the representative local pattern in the typical sample images can be extracted in advance as the initialization value of the convolution kernel so that the convolution kernels may be matched with the local patterns of the training samples, which can accelerate the convergence speed of the network and improve the recognition accuracy of the network.