mnist手写数字matlab
时间: 2023-09-15 12:23:04 浏览: 47
这是一个使用Matlab实现MNIST手写数字识别的代码示例:
```
% Load MNIST dataset
load mnist_all.mat
% Concatenate training and testing data
train_data = double([train0;train1;train2;train3;train4;train5;train6;train7;train8;train9]);
train_labels = double([zeros(size(train0,1),1);ones(size(train1,1),1);2*ones(size(train2,1),1);3*ones(size(train3,1),1);4*ones(size(train4,1),1);5*ones(size(train5,1),1);6*ones(size(train6,1),1);7*ones(size(train7,1),1);8*ones(size(train8,1),1);9*ones(size(train9,1),1)]);
test_data = double([test0;test1;test2;test3;test4;test5;test6;test7;test8;test9]);
test_labels = double([zeros(size(test0,1),1);ones(size(test1,1),1);2*ones(size(test2,1),1);3*ones(size(test3,1),1);4*ones(size(test4,1),1);5*ones(size(test5,1),1);6*ones(size(test6,1),1);7*ones(size(test7,1),1);8*ones(size(test8,1),1);9*ones(size(test9,1),1)]);
% Normalize the data
train_data = train_data/255;
test_data = test_data/255;
% Train a support vector machine (SVM) classifier
svm = fitcecoc(train_data,train_labels);
% Test the classifier on the test data
predicted_labels = predict(svm,test_data);
% Calculate the accuracy of the classifier
accuracy = sum(predicted_labels == test_labels)/length(test_labels);
% Display the accuracy
disp(['Accuracy: ' num2str(accuracy*100) '%']);
```
这段代码加载MNIST数据集,将训练和测试数据拼接起来,对数据进行归一化处理,使用支持向量机(SVM)分类器进行训练和测试,最后计算分类器的准确度并将其显示出来。