# Research on a Convolution Kernel Initialization Method for Speeding Up the Convergence of CNN
## 4 Conclusions  
The convolution kernel initialization of CNN is very important to accelerate the convergence speed of CNN and obtain high classification accuracy. The random initialization method is simple and direct, but the convergence speed of the network is slow, and the network may fall into the local optimization. In the most severe cases, gradient dispersion may be caused. The Xavier initialization method is not applicable to non-linear activation functions such as Relu. Although the He initialization method is proposed for the Relu activation function, the matching between the convolution kernel and the local pattern of the training sample images is a small probability event, and the network needs many iterations to converge to the minimum value. Based on the above reasons, the convolution kernel initialization method based on LBP and SAE was proposed in this paper. The CIFAR-10 dataset was used as the experimental dataset. Firstly, the images whose dynamic range and image entropy were greater than the mean value in the dataset were selected as typical images. Ten images were randomly selected from each category of the typical images. Then, the LBP algorithm was used to extract the texture features of these images. These texture feature images were divided into many small blocks. These blocks are the local pattern of the typical images. Finally, these small blocks were input into the sparse autoencoder for pre-training. After finishing the pre-training, the first layer weight values in the sparse autoencoder were extracted as the convolution kernel initial value of the first layer of CNN. The size of the image in the training set was 32 \(\times\) 32. The experimental results showed that the convergence speed and the recognition rate both improved for small-sized images. During the experiment, there was no absolute relationship with the size of the image. For the convenience of calculation, the square image was selected as the training set. Therefore, the large-size image can also get the same experimental results, but the running time of the program may be prolonged in the process of dividing the texture image into small blocks. In summary, under the same experimental conditions, this method sped up the convergence of the network to an extent in the network training process and guided the optimization direction of the network. At the same time, the recognition rate of the network was improved to an extent. The experimental result also indicated that in the problem of the convolution kernel initialization, the representative local patterns in the training images might be extracted in advance as the initial value of the convolution kernel. The method proposed in this paper also has some problems and shortcomings. After completing the LBP algorithm, these texture feature images are divided into many small blocks. The size of the small blocks is 3 \(\times\) 3 so the probability of a complete match between the block and the target to be identified is small, resulting in a small improvement in the accuracy of recognition. It can be considered to appropriately increase the size of the block to increase the matching degree between the block and the target to be identified to improve the accuracy of network recognition. In addition, the method proposed in this paper only initializes the first layer convolution kernel of CNN. It can be considered to improve the method to obtain the initial value of the other layers' convolution kernels in order to further accelerate the convergence speed of the network. Finally, the method proposed in this paper is relatively cumbersome in obtaining the initial value of the convolution kernel. It can be considered to build a platform for obtaining the initial value of the convolution kernel. Users can directly input the dataset and the corresponding parameters into the platform and directly obtain the initial value of the CNN convolution kernel through platform calculation. The above three aspects are the main content of the next research work.