An error based adaptive learning rate stochastic gradient descent algorithm in convolutional neural network
Qianyi Li · Applied and Computational Engineering · 2023
If the learning rate of convolutional neural network (CNN) is set improperly, the efficiency and accuracy of the algorithm will be greatly affected. To solve this problem, a learning rate adaptive algorithm is proposed to improve the traditional SGD: based on parameter prediction, the historical training error is used to update the learning rate. Under the condition of given initial learning rate, experiments on classical data sets prove the effectiveness of the above algorithms, and the adaptive learning rate stochastic gradient descent algorithm can keep the convergence of the network. Training accuracy is relatively stable; Shorter training time; And improve the learning accuracy.