基于改进的S-ReLU激活函数的图像分类方法

为解决ReLU函数负区域取值为0而引发的对应权重无法更新的问题，提出了新的激活函数S-ReLU。该函数在负区域具有软饱和性，增加了负样本数据的关注度。通过赋予负区域输出值较小的导数，促进了负输入值的反向传播，提高了模型的鲁棒性。通过与其他常见激活函数在数据集MNIST、CIFAR-10上使用LeNet-5模型的对比实验，探究基于S-ReLU激活函数的图像分类效果。实验结果表明，对于MNIST和CIFAR-10数据集，相比使用其他激活函数，S-ReLU函数提高了模型的分类精度。

Image Classification Method Based on Improved S-ReLUActivation Function

In order to solve the problem that the corresponding weight cannot be updated due to the value of 0 in the negative region of ReLU function, a new activation function S-ReLU is proposed. It has soft saturation in the negative region and increases the attention of negative sample data. By giving a smaller derivative to the output value in the negative region, it promotes the back propagation of negative input value, the robustness of the model is improved. Through the comparative experiment of using LeNet-5 model on MNIST and CIFAR-10 datasets with other common activation functions, the image classification effect based on S-ReLU activation function is explored. The experimental results show that for MNIST and CIFAR-10 datasets, using S-ReLU function improves the classification accuracy of the model compared with other activation functions.