matlab通过简单的步骤对自己数据集实现SVM
1、首先用简单的matlab提供的数据集进行测试
% 下载数据集：这里先用matlab自带的数据集
unzip('MerchData.zip');
imds = imageDatastore('MerchData',"IncludeSubfolders",true,'LabelSource','foldernames');
[imdsTrain,imdsTest] = splitEachLabel(imds,0.7,"randomized");
% 显示一些图像
numTrainImages = numel(imdsTrain.Labels);
% 随机返回16张图像
idx = randperm(numTrainImages,16);
figure
for i = 1:16
subplot(4,4,i)
% I = imread(imdsTrain.Files{i,1});
I = readimage(imdsTrain,idx(i));
imshow(I)
end
net = alexnet
net.Layers
% 输出训练网络的1000类中的前10个
net.Layers(end).Classes(1:10)
inputSize = net.Layers(1).InputSize
% 分析网络架构
analyzeNetwork(net)
% 也可以增加数据增强操作，增加训练的复杂度
% imageDataAugmenter = imageDataAugmenter(...
% 'RandRotation',[-20,20],...
% 'RandXTranslation',[-3,3],...
% 'RandYTranslation',[-3,3])
% augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain,"DataAugmentation",imageDataAugmenter);
augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain);
% 测试集就不需要添加增强操作了
augimdsTest = augmentedImageDatastore(inputSize(1:2),imdsTest);
% 较深层的特征
layer = 'fc7';
featuresTrain = activations(net,augimdsTrain,layer,'OutputAs','rows');
featuresTest = activations(net,augimdsTest,layer,'OutputAs','rows');
% 查看信息
whos featuresTrain
% 获取训练数据和测试数据的类标签
YTrain = imdsTrain.Labels
YTest = imdsTest.Labels
% 拟合图像分类器
classifier = fitcecoc(featuresTrain,YTrain)
% 对经过训练的SVM模型从测试图像中提取的特征对测试图像进行分类
YPred = predict(classifier,featuresTest);
% 返回一些图像
idx = [1 5 10 15];
figure
for i = 1:numel(idx)
subplot(2,2,i)
I = readimage(imdsTest,idx(i));
label = YPred(idx(i));
imshow(I)
title(char(label))
end
% 针对测试集的分类准确度
accuracy = mean(YPred == YTest)
% 基于较浅特征训练分类器
layer = 'relu3';
% 这里就不需要下面的激活平均操作了，如果没有的话仍然需要下面的操作
featuresTrain = activations(net,augimdsTrain,layer,"OutputAs","rows");
featuresTest = activations(net,augimdsTest,layer,'OutputAs','rows');
whos featuresTrain
% % 手动对所有空间位置的激活区域求平均：最后要获得N*C形式的特征，其中N是观测值数目，C是特征数量
% 下面有对应的转置操作
% featuresTrain = squeeze(mean(featuresTrain,[1,2]))';
% featuresTest = squeeze(mean(featuresTest,[1,2]))';
% whos featuresTrain
% 训练SVM分类器
classifier = fitcecoc(featuresTrain,YTrain);
YPred = predict(classifier,featuresTest);
% 测试准确率
accuracy = mean(YPred == YTest)输出结果：
net =
SeriesNetwork - 属性:
Layers: [25×1 nnet.cnn.layer.Layer]
InputNames: {'data'}
OutputNames: {'output'}
ans =
具有以下层的 25x1 Layer 数组:
1 'data' 图像输入 227x227x3 图像: 'zerocenter' 归一化
2 'conv1' 卷积 96 11x11x3 卷积: 步幅 [4 4]，填充 [0 0 0 0]
3 'relu1' ReLU ReLU
4 'norm1' 跨通道归一化 跨通道归一化: 每元素 5 个通道
5 'pool1' 最大池化 3x3 最大池化: 步幅 [2 2]，填充 [0 0 0 0]
6 'conv2' 分组卷积 2 groups of 128 5x5x48 卷积: 步幅 [1 1]，填充 [2 2 2 2]
7 'relu2' ReLU ReLU
8 'norm2' 跨通道归一化 跨通道归一化: 每元素 5 个通道
9 'pool2' 最大池化 3x3 最大池化: 步幅 [2 2]，填充 [0 0 0 0]
10 'conv3' 卷积 384 3x3x256 卷积: 步幅 [1 1]，填充 [1 1 1 1]
11 'relu3' ReLU ReLU
12 'conv4' 分组卷积 2 groups of 192 3x3x192 卷积: 步幅 [1 1]，填充 [1 1 1 1]
13 'relu4' ReLU ReLU
14 'conv5' 分组卷积 2 groups of 128 3x3x192 卷积: 步幅 [1 1]，填充 [1 1 1 1]
15 'relu5' ReLU ReLU
16 'pool5' 最大池化 3x3 最大池化: 步幅 [2 2]，填充 [0 0 0 0]
17 'fc6' 全连接 4096 全连接层
18 'relu6' ReLU ReLU
19 'drop6' 丢弃 50% 丢弃
20 'fc7' 全连接 4096 全连接层
21 'relu7' ReLU ReLU
22 'drop7' 丢弃 50% 丢弃
23 'fc8' 全连接 1000 全连接层
24 'prob' Softmax softmax
25 'output' 分类输出 crossentropyex: 具有 'tench' 和 999 个其他类ans = 10×1 categorical 数组
tench
goldfish
great white shark
tiger shark
hammerhead
electric ray
stingray
cock
hen
ostrich
inputSize = 1×3
227 227 3
Name Size Bytes Class Attributes
featuresTrain 55x4096 901120 single
YTrain = 55×1 categorical 数组
MathWorks Cap
MathWorks Cap
MathWorks Cap
MathWorks Cap
MathWorks Cap
MathWorks Cap
MathWorks Cap
MathWorks Cap
MathWorks Cap
MathWorks Cap
YTest = 20×1 categorical 数组
MathWorks Cap
MathWorks Cap
MathWorks Cap
MathWorks Cap
MathWorks Cube
MathWorks Cube
MathWorks Cube
MathWorks Cube
MathWorks Playing Cards
MathWorks Playing Cards
classifier =
ClassificationECOC
ResponseName: 'Y'
CategoricalPredictors: []
ClassNames: [MathWorks Cap MathWorks Cube MathWorks Playing Cards MathWorks Screwdriver MathWorks Torch]
ScoreTransform: 'none'
BinaryLearners: {10×1 cell}
CodingName: 'onevsone'
Properties, Method
accuracy = 1
Name Size Bytes Class Attributes
featuresTrain 55x64896 14277120 single
accuracy = 0.90002、再针对自己的数据集进行测试
现在开始对自己的数据集进行测试：Corel1K，网上能下载到，如果想要数据集的话，可以留言我看到发给你！
这个数据集总计10个类，每个类100张图像
Corel1Kdataset = imageDatastore('Corel1K');
labels = zeros(1000,1);
filename = dir('./Corel1K/*.jpg');
for i = 1:1000
split = strsplit(filename(i).name,{'_','.'});
labels(i) = str2double(split{1});
end
Corel1Kdataset.Labels = categorical(labels);
[imdsTrain,imdsTest] = splitEachLabel(Corel1Kdataset,0.7,"randomized");
% 显示一些图像
numTrainImages = numel(imdsTrain.Labels);
% 随机返回16张图像
idx = randperm(numTrainImages,16);
figure
for i = 1:16
subplot(4,4,i)
% I = imread(imdsTrain.Files{i,1});
I = readimage(imdsTrain,idx(i));
imshow(I)
end
net = vgg16;
net.Layers
net.Layers(end).Classes(1:10)
inputSize = net.Layers(1).InputSize
% 分析网络架构
analyzeNetwork(net)
augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain);
% 测试集就不需要添加增强操作了
augimdsTest = augmentedImageDatastore(inputSize(1:2),imdsTest);
% 较深层的特征
layer = 'fc7';
featuresTrain = activations(net,augimdsTrain,layer,'OutputAs','rows',"ExecutionEnvironment","cpu");
featuresTest = activations(net,augimdsTest,layer,'OutputAs','rows','ExecutionEnvironment',"cpu");
whos featuresTrain
% 获取训练数据和测试数据的类标签
YTrain = imdsTrain.Labels;
YTest = imdsTest.Labels;
% 拟合图像分类器
classifier = fitcknn(featuresTrain,YTrain,"NumNeighbors",5);
% 对经过训练的SVM模型从测试图像中提取的特征对测试图像进行分类
YPred = predict(classifier,featuresTest);
% 返回一些图像
idx = [1 5 10 15];
figure
for i = 1:numel(idx)
subplot(2,2,i)
I = readimage(imdsTest,idx(i));
label = YPred(idx(i));
imshow(I)
title(char(label))
end
% 针对测试集的分类准确度
accuracy = mean(YPred == YTest)
输出结果：accuracy = 0.9667