我正在寻找在神经网络中应用10倍交叉验证的示例。我需要这个问题的链接答案:Example of 10-fold SVM classification in MATLAB
我想对所有3个类进行分类,而在示例中只考虑了两个类。
编辑:这是我为iris示例编写的代码
load fisheriris %# load iris dataset
k=10;
cvFolds = crossvalind('Kfold', species, k); %# get indices of 10-fold CV
net = feedforwardnet(10);
for i = 1:k %# for each fold
testIdx = (cvFolds == i); %# get indices of test instances
trainIdx = ~testIdx; %# get indices training instances
%# train
net = train(net,meas(trainIdx,:)',species(trainIdx)');
%# test
outputs = net(meas(trainIdx,:)');
errors = gsubtract(species(trainIdx)',outputs);
performance = perform(net,species(trainIdx)',outputs)
figure, plotconfusion(species(trainIdx)',outputs)
end
错误由matlab提供:
Error using nntraining.setup>setupPerWorker (line 62)
Targets T{1,1} is not numeric or logical.
Error in nntraining.setup (line 43)
[net,data,tr,err] = setupPerWorker(net,trainFcn,X,Xi,Ai,T,EW,enableConfigure);
Error in network/train (line 335)
[net,data,tr,err] = nntraining.setup(net,net.trainFcn,X,Xi,Ai,T,EW,enableConfigure,isComposite);
Error in Untitled (line 17)
net = train(net,meas(trainIdx,:)',species(trainIdx)');
答案 0 :(得分:5)
使用MATLAB的crossval
函数要比使用crossvalind
手动操作简单得多。由于您只是询问如何从交叉验证中获得测试“得分”,而不是使用它来选择最佳参数,例如隐藏节点的数量,您的代码将如此简单:
load fisheriris;
% // Split up species into 3 binary dummy variables
S = unique(species);
O = [];
for s = 1:numel(S)
O(:,end+1) = strcmp(species, S{s});
end
% // Crossvalidation
vals = crossval(@(XTRAIN, YTRAIN, XTEST, YTEST)fun(XTRAIN, YTRAIN, XTEST, YTEST), meas, O);
剩下的就是写一个函数fun
,它接受输入和输出训练和测试集(所有这些都由crossval
函数提供给你,所以你不必担心分裂你的数据自己),在训练集上训练神经网络,在测试集上测试它,然后使用您的首选指标输出分数。所以像这样:
function testval = fun(XTRAIN, YTRAIN, XTEST, YTEST)
net = feedforwardnet(10);
net = train(net, XTRAIN', YTRAIN');
yNet = net(XTEST');
%'// find which output (of the three dummy variables) has the highest probability
[~,classNet] = max(yNet',[],2);
%// convert YTEST into a format that can be compared with classNet
[~,classTest] = find(YTEST);
%'// Check the success of the classifier
cp = classperf(classTest, classNet);
testval = cp.CorrectRate; %// replace this with your preferred metric
end
我没有神经网络工具箱所以我无法测试这个我害怕。但它应该证明这一原则。