给我一个深度学习图像识别的MATLAB代码
时间: 2023-05-28 20:07:57 浏览: 67
以下是一个使用深度学习进行图像识别的MATLAB代码示例:
```
% 加载图像数据
imds = imageDatastore('路径', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');
% 将数据集分为训练集和测试集
[imdsTrain, imdsTest] = splitEachLabel(imds, 0.7, 'randomized');
% 定义卷积神经网络
layers = [
imageInputLayer([224 224 3])
convolution2dLayer(3, 64, 'Padding', 'same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2, 'Stride', 2)
convolution2dLayer(3, 128, 'Padding', 'same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2, 'Stride', 2)
convolution2dLayer(3, 256, 'Padding', 'same')
batchNormalizationLayer
reluLayer
convolution2dLayer(3, 256, 'Padding', 'same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2, 'Stride', 2)
convolution2dLayer(3, 512, 'Padding', 'same')
batchNormalizationLayer
reluLayer
convolution2dLayer(3, 512, 'Padding', 'same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2, 'Stride', 2)
convolution2dLayer(3, 512, 'Padding', 'same')
batchNormalizationLayer
reluLayer
convolution2dLayer(3, 512, 'Padding', 'same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2, 'Stride', 2)
fullyConnectedLayer(4096)
reluLayer
dropoutLayer(0.5)
fullyConnectedLayer(4096)
reluLayer
dropoutLayer(0.5)
fullyConnectedLayer(2)
softmaxLayer
classificationLayer
];
% 定义训练选项
options = trainingOptions('sgdm', ...
'MiniBatchSize', 32, ...
'MaxEpochs', 20, ...
'InitialLearnRate', 1e-4, ...
'Shuffle', 'every-epoch', ...
'ValidationData', imdsTest, ...
'ValidationFrequency', 10, ...
'Verbose', true, ...
'Plots', 'training-progress');
% 训练卷积神经网络
net = trainNetwork(imdsTrain, layers, options);
% 对测试集进行预测并计算准确率
YPred = classify(net, imdsTest);
YTest = imdsTest.Labels;
accuracy = sum(YPred == YTest) / numel(YTest);
disp("准确率:" + accuracy);
```
该代码使用了一个基于VGG16的卷积神经网络来对图像数据进行分类。首先,将图像数据加载到一个ImageDatastore中,并将其分为训练集和测试集。然后,定义了一个包含卷积层、池化层、全连接层和分类层的卷积神经网络,并使用sgdm优化器进行训练。最后,对测试集进行预测并计算准确率。
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