基于改进Sigmoid卷积神经网络的手写体数字识别

Handwritten Numeral Recognition Based on Improved Sigmoid Convolutional Neural Network

深度学习技术在数字识别领域有着普遍的应用。通过深度学习技术构造神经网络模型,运用不同的激活函数搭配不同的参数初始化策略,对MINIST手写数据集进行训练;构建分析模型,识别图像中的数字,将大数据量的图片降维成小数据量图片,同时保证能够有效保留图片特征;通过对图片数据的分析,加入特征转换过程,利用梯度下降优化器,搭建网络结构,将数据降维,有效地避免过拟合;利用交叉熵验证对模型进行编译和训练,对输出的分类结果进行进一步分析,在Sigmoid激活函数的输出层,通过K最近邻分类算法,设置KNN分类器,进一步提高了分类预测的准确率。MNIST数据集上的实验结果显示识别率为96.2%,在输出层引入K最近邻算法KNN(K-Nearst Neighbors)结合传统卷积神经网络(Convolutional Neural Network,CNN)的全连接层与softmax层,经交叉验证得到99.6%的识别率。

Deep learning technology is widely used in the field of number recognition.It constructs neural network model through deep learning technology,nonlinear transformation activation function in neurons,different activation functions with different parameter initialization strategies,trains MINIST handwritten data set,constructs analysis model and recognizes numbers in images,reduce the dimension of a large amount of data into a small amount of data,and ensure the effective retention of image features.Through the analysis of image data,adding the feature conversion process,using the gradient descent optimizer to build a network structure and reduce the dimension of data,which can effectively avoid over fitting.Cross-entropy verification is used to compile and train the model,and the output classification results are further analyzed.Through the K-nearest neighbor classification algorithm,KNN classifier is set to further improve the accuracy of classification and prediction.Through MNIST data set experiment,the recognition rate is about 96.2%.The K-nearest neighbor algorithm(KNN)is introduced into the output layer,combined with the full connection layer and softmax layer of traditional convolutional neural network(CNN).After cross verification,the recognition rate is 99.6%.