一种改进池化模型对卷积神经网络性能影响的研究

池化模型作为卷积神经网络模型中至关重要的一部分，具有降维、提高模型泛化能力等作用。为了进一步提高卷积神经网络模型的准确率，优化模型的学习性能，提出了一种基于最大池化和平均池化的改进池化模型，并在全球手写数字数据集MNIST和CIFAR-10上分别对改进池化模型的有效性进行了验证。通过与常见池化模型的对比实验发现，采用改进池化模型的卷积神经网络的学习性能较优，一次迭代情况下，在MNIST和CIFAR-10数据集上，错误率分别下降了4.28%和2.15%。

Research on the influence of an improved pooling model on the performance of convolutional neural networks

As a vital part of the convolutional neural network model, the pooling model has the functions of dimension reduction and generalization of the model. In order to further improve the accuracy of the convolutional neural network model and optimize the learning performance of the model, this paper proposes an improved pooling model based on maximum pooling and average pooling, and the global handwritten digital datasets MNIST and CIFAR-10 data. The effectiveness of the improved pooling model was verified on the two dataset. Comparing with the common pooling model, it is found that the learning performance of the convolutional neural network with improved pooling model is better. In one iteration, the error rate decreases by 4.28% on the MNIST and decreases by 2.15% on CIFAR-10 datasets.