采用分数阶动量的卷积神经网络随机梯度下降法

针对随机梯度下降法可能会收敛到局部最优的问题,文中提出采用分数阶动量的随机梯度下降法,提高卷积神经网络的识别精度和学习收敛速度.结合基于动量的随机梯度下降法和分数阶差分运算,改进参数更新方法,讨论分数阶阶次对网络参数训练效果的影响,给出阶次调整方法.在MNIST、CIFAR-10数据集上的实验表明,文中方法可以提高卷积神经网络的识别精度和学习收敛速度.

Stochastic Gradient Descent Method of Convolutional Neural Network Using Fractional-Order Momentum

The stochastic gradient descent method may converge to a local optimum. Aiming at this problem, a stochastic gradient descent method of convolutional neural network using fractional-order momentum is proposed to improve recognition accuracy and learning convergence rate of convolution neural networks. By combining the traditional momentum-based stochastic gradient descent method with fractional-order difference method, the parameter updating method is improved. The influence of fractional-order on the training result of network parameters is discussed, and an order adjustment method is produced. The validity of the proposed parameters training method is verified and analyzed on MNIST dataset and CIFAR-10 dataset. The experimental results show that the proposed method improves the recognition accuracy and learning convergence rate of convolutional neural networks.