AlexNet网络混淆矩阵具体matlab
时间: 2023-12-21 14:06:37 浏览: 30
实现代码如下:
```matlab
% 加载数据集
imds = imageDatastore('path/to/images', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');
% 分割数据集
[imdsTrain, imdsValidation] = splitEachLabel(imds, 0.7, 'randomize');
% 定义网络架构
layers = [
imageInputLayer([227 227 3])
convolution2dLayer(11, 96, 'Stride', 4, 'Padding', 0)
reluLayer()
maxPooling2dLayer(3, 'Stride', 2)
crossChannelNormalizationLayer(5)
convolution2dLayer(5, 256, 'Stride', 1, 'Padding', 2)
reluLayer()
maxPooling2dLayer(3, 'Stride', 2)
crossChannelNormalizationLayer(5)
convolution2dLayer(3, 384, 'Stride', 1, 'Padding', 1)
reluLayer()
convolution2dLayer(3, 384, 'Stride', 1, 'Padding', 1)
reluLayer()
convolution2dLayer(3, 256, 'Stride', 1, 'Padding', 1)
reluLayer()
maxPooling2dLayer(3, 'Stride', 2)
fullyConnectedLayer(4096)
reluLayer()
dropoutLayer(0.5)
fullyConnectedLayer(4096)
reluLayer()
dropoutLayer(0.5)
fullyConnectedLayer(numClasses)
softmaxLayer()
classificationLayer()];
% 设置训练选项
options = trainingOptions('sgdm', ...
'MiniBatchSize', 128, ...
'MaxEpochs', 20, ...
'InitialLearnRate', 0.01, ...
'LearnRateSchedule', 'piecewise', ...
'LearnRateDropFactor', 0.1, ...
'LearnRateDropPeriod', 10, ...
'Shuffle', 'every-epoch', ...
'ValidationData', imdsValidation, ...
'ValidationFrequency', 30, ...
'Verbose', false, ...
'Plots', 'training-progress');
% 训练网络
net = trainNetwork(imdsTrain, layers, options);
% 对验证集进行分类
YPred = classify(net, imdsValidation);
YValidation = imdsValidation.Labels;
% 计算混淆矩阵
confusionMatrix = confusionmat(YValidation, YPred);
% 显示混淆矩阵
figure
heatmap(confusionMatrix, unique(YValidation), unique(YValidation), 1, 'Colormap', 'red', 'Colorbar', true);
xlabel('Predicted Label');
ylabel('True Label');
title('Confusion Matrix');
```
其中,`path/to/images`需要替换为实际的图片路径,`numClasses`需要替换为数据集的类别数。执行以上代码即可得到AlexNet网络在验证集上的混淆矩阵。