我正在尝试通过patternnet神经网络使用k倍交叉验证。
inputs1是特征向量，targets1是来自'iris_dataset'的标签向量。并且xtrain，xtest，ytrain和ytest分别是使用cvpartition函数分割后的训练和测试功能和标签。
步骤如下：
1。首先，创建了模式识别网络（patternnet）。
In the first and second scripts: net = patternnet;
2。使用cvpartition将数据划分为k折后，将创建两个训练和测试功能以及标签（k=10）。
In the first and second scripts: fold = cvpartition(targets_Vec, 'kfold', kfold);
3。然后，configure命令用于配置网络对象，并初始化网络的权重和偏差。
In the first script: net = configure(net, xtrain', dummyvar(ytrain)'); % xtrain and ytrain are features and labels from step (2).
或
In the second script: net = configure(net, inputs1, targets1); % inputs1 and targets1 are features and labels before splitting up.
4。初始化参数和超参数后，使用训练数据（通过train()函数对网络进行训练。
In the first script: [net, tr] = train(net, xtrain', dummyvar(ytrain)'); % xtrain and ytrain are features and labels from step (2).
或
In the second script: [net, tr] = train(net, inputs1, targets1); % inputs1 and targets1 are features and labels before splitting up.
5。最后，使用经过训练的网络（通过net()函数估算目标）。
In the first script: pred = net(xtest'); % testing features from step (2).
或
In the second script: pred = net(inputs1);
由于训练和测试功能是使用cvpartition分开的，因此应该使用训练功能及其标签来训练网络，然后再通过测试功能（新数据）对其进行测试。
尽管train()函数用于训练网络，但是它将自己的输入（来自步骤（2）的训练数据和标签）分为训练，验证和测试数据，而来自步骤的原始测试数据（2）仍未使用。
因此，我需要一个用于训练步骤2（训练和验证）中的功能和标签以进行学习的功能，以及另一个用于对新数据进行分类的功能（来自步骤2的测试功能）。
搜索后，我写了两个脚本，我认为第一个脚本不正确，但是我不确定第二个脚本也不正确吗？如何解决？
第一个脚本：
clc; close all; clearvars;
load iris_dataset
max_iter = 10;
kfold = 10;
[inputs, targets] = iris_dataset;
inputs = inputs';
targets = targets';
targets_Vec= [];
for j = 1 : size(targets, 1)
if max(targets(j, 1:3) == 1) && find(targets(j, 1:3))==1
targets_Vec = [targets_Vec; 1];
elseif max(targets(j, 1:3) == 1) && find(targets(j, 1:3))==2
targets_Vec = [targets_Vec; 2];
elseif max(targets(j, 1:3) == 1) && find(targets(j, 1:3))==3
targets_Vec = [targets_Vec; 3];
end
end
net = patternnet; ... Create a Pattern Recognition Network
rng('default');
... Divide data into k-folds
fold = cvpartition(targets_Vec, 'kfold', kfold);
... Pre
pred2 = []; ytest2 = []; Afold = zeros(kfold,1);
... Neural network start
for k = 1 : kfold
... Call index of training & testing sets
trainIdx = fold.training(k); testIdx = fold.test(k);
... Call training & testing features and labels
xtrain = inputs(trainIdx,:); ytrain = targets_Vec(trainIdx);
xtest = inputs(testIdx,:); ytest = targets_Vec(testIdx);
... configure
net = configure(net, xtrain', dummyvar(ytrain)');
... Initialize neural network
net.layers{1}.name='Hidden Layer 1';
net.layers{2}.name='Output Layer';
net.layers{1}.size = 20;
net.layers{1}.transferFcn = 'tansig';
net.trainFcn = 'trainscg';
net.performFcn = 'crossentropy';
... Choose Input and Output Pre/Post-Processing Functions
net.input.processFcns = {'removeconstantrows','mapminmax'};
net.output.processFcns = {'removeconstantrows','mapminmax'};
... Train the Network
[net, tr] = train(net, xtrain', dummyvar(ytrain)');
... Estimate the targets using the trained network.(Test)
pred = net(xtest');
... Confusion matrix
[c, cm] = confusion(dummyvar(ytest)',pred);
... Get accuracy for each fold
Afold(k) = 100*sum(diag(cm))/sum(cm(:));
... Store temporary result for each fold
pred2 = [pred2(1:end,:), pred];
ytest2 = [ytest2(1:end); ytest];
end
... Overall confusion matrix
[~,confmat] = confusion(dummyvar(ytest2)', pred2);
confmat=transpose(confmat);
... Average accuracy over k-folds
acc = mean(Afold);
... Store results
NN.fold = Afold;
NN.acc = acc;
NN.con = confmat;
fprintf('\n Final classification Accuracy (NN): %g %%',acc);
第二个脚本：
clc; close all; clearvars;
load iris_dataset
max_iter = 10;
kfold = 10;
[inputs1, targets1] = iris_dataset;
inputs = inputs1';
targets = targets1';
targets_Vec= [];
for j = 1 : size(targets, 1)
if max(targets(j, 1:3) == 1) && find(targets(j, 1:3))==1
targets_Vec = [targets_Vec; 1];
elseif max(targets(j, 1:3) == 1) && find(targets(j, 1:3))==2
targets_Vec = [targets_Vec; 2];
elseif max(targets(j, 1:3) == 1) && find(targets(j, 1:3))==3
targets_Vec = [targets_Vec; 3];
end
end
net = patternnet; ... Create a Pattern Recognition Network
rng('default');
... Divide data into k-folds
fold = cvpartition(targets_Vec, 'kfold', kfold);
... Pre
pred2 = []; ytest2 = []; Afold = zeros(kfold,1);
... Neural network start
for k = 1 : kfold
... Call index of training & testing sets
trainIdx = fold.training(k); testIdx = fold.test(k);
... Call training & testing features and labels
xtrain = inputs(trainIdx,:); ytrain = targets_Vec(trainIdx);
xtest = inputs(testIdx,:); ytest = targets_Vec(testIdx);
... configure
net = configure(net, inputs1, targets1);
trInd = find(trainIdx); tstInd = find(testIdx);
net.divideFcn = 'divideind';
net.divideParam.trainInd = trInd;
net.divideParam.testInd = tstInd;
... Initialize neural network
net.layers{1}.name='Hidden Layer 1';
net.layers{2}.name='Output Layer';
net.layers{1}.size = 20;
net.layers{1}.transferFcn = 'tansig';
net.trainFcn = 'trainscg';
net.performFcn = 'crossentropy';
... Choose Input and Output Pre/Post-Processing Functions
net.input.processFcns = {'removeconstantrows','mapminmax'};
net.output.processFcns = {'removeconstantrows','mapminmax'};
... Train the Network
[net, tr] = train(net, inputs1, targets1);
pred = net(inputs1); ... Estimate the targets using the trained network (Test)
... Confusion matrix
[c, cm] = confusion(targets1, pred);
y = net(inputs1);
e = gsubtract(targets1, y);
performance = perform(net, targets1, y);
tind = vec2ind(targets1);
yind = vec2ind(y);
percentErrors = sum(tind ~= yind)/numel(tind);
... Recalculate Training, Validation and Test Performance
trainTargets = targets1 .* tr.trainMask{1};
% valTargets = targets1 .* tr.valMask{1};
testTargets = targets1 .* tr.testMask{1};
trainPerformance = perform(net, trainTargets, y);
% valPerformance = perform(net, valTargets, y);
testPerformance = perform(net, testTargets, y);
test_Fold(k) = testPerformance;
end
test_Fold_mean = mean(test_Fold);
acc = 100*(1-test_Fold_mean);
fprintf('\n Final classification Accuracy (NN): %g %%',acc);