Set Up Your C Compiler

To generate C/C++ code, you must have access to a C/C++ compiler that is configured properly. MATLAB Coder™ locates and uses a supported, installed compiler. You can usemex-setupto view and change the default compiler. For more details, seeChange Default Compiler。

Assumptions and Limitations

To generate C code, MATLAB Coder:

Concerning the last limitation, consider that:

To work around the code generation limitations for classification, train the classification model using MATLAB, then pass the resulting model object tosaveLearnerForCoder。这saveLearnerForCoderfunction removes some properties that are not required for prediction, and then saves the trained model to disk as a structure array. Like the model, the structure array contains the information used to classify new observations.

After saving the model to disk, load the model in the MATLAB function by usingloadLearnerForCoder。这loadLearnerForCoderfunction loads the saved structure array, and then reconstructs the model object. In the MATLAB function, to classify the observations, you can pass the model and predictor data set, which can be an input argument of the function, topredict。

Code Generation for Classification Workflow

在将图像分类部署到设备上之前：

Load Data

加载digitimagesdata set.

loaddigitimages

imagesis a 28-by-28-by-3000 array ofuint16integers. Each page is a raster image of a digit. Each element is a pixel intensity. Corresponding labels are in the 3000-by-1 numeric vectorY。For more details, enterDescriptionat the command line.

Store the number of observations and number of predictor variables. Create a data partition that specifies to hold out 20% of the data. Extract training and test set indices from the data partition.

rng(1)再现性的百分比n = size(images,3); p = numel(images(:,:,1)); cvp = cvpartition(n,'Holdout',0.20); idxTrn = training(cvp); idxTest = test(cvp);

Display nine random images from the data.

figureforj = 1:9 subplot(3,3,j) selectImage = datasample(images,1,3); imshow(selectImage,[])end

Rescale Data

Because raw pixel intensities vary widely, you should normalize their values before training a classification model. Rescale the pixel intensities so that they range in the interval [0,1]. That is, suppose $$p_{ij}$$is pixel intensity $$j$$within image $$i$$。For image $$i$$, rescale all of its pixel intensities using this formula:

X = double(images);fori = 1:n minX = min(min(X(:,:,i))); maxX = max(max(X(:,:,i))); X(:,:,i) = (X(:,:,i) - minX)/(maxX - minX);end

Alternatively, if you have an Image Processing Toolbox™ license, then you can efficiently rescale pixel intensities of images to [0,1] by usingmat2gray。For more details, seemat2gray(Image Processing Toolbox)。

Reshape Data

For code generation, the predictor data for training must be in a table of numeric variables or a numeric matrix.

Reshape the data to a matrix such that predictor variables (pixel intensities) correspond to columns, and images (observations) to rows. Becausereshape需要元素列，您必须转换结果。

X = reshape(X,[p,n])';

To ensure that preprocessing the data maintains the image, plot the first observation inX。

figure imshow(reshape(X(1,:),sqrt(p)*[1 1]),[],'InitialMagnification','fit')

Extract Features

Computer Vision Toolbox™ offers several feature-extraction techniques for images. One such technique is the extraction of histogram of oriented gradient (HOG) features. To learn how to train an ECOC model using HOG features, see数字Classification Using HOG Features(Computer Vision Toolbox)。For details on other supported techniques, seeLocal Feature Detection and Extraction(Computer Vision Toolbox)。This example uses the rescaled pixel intensities as predictor variables.

Train and Optimize Classification Model

Linear SVM models are often applied to image data sets for classification. However, SVM are binary classifiers, and there are 10 possible classes in the data set.

您可以使用多种二进制SVM学习者创建多种多组模型fitcecoc。fitcecoccombines multiple binary learners using a coding design. By default,fitcecocapplies the one-versus-one design, which specifies training binary learners based on observations from all combinations of pairs of classes. For example, in a problem with 10 classes,fitcecocmust train 45 binary SVM models.

In general, when you train a classification model, you should tune the hyperparameters until you achieve a satisfactory generalization error. That is, you should cross-validate models for particular sets of hyperparameters, and then compare the out-of-fold misclassification rates.

You can choose your own sets of hyperparameter values, or you can specify to implement Bayesian optimization. (For general details on Bayesian optimization, seeBayesian Optimization Workflow。) This example performs cross-validation over a chosen grid of values.

要根据培训观察跨越SVM二进制学习者的ECOC模型，使用5倍交叉验证。虽然预测值值具有相同的范围，但是为了避免在训练期间的数值困难，标准化预测器。此外，优化ECOC编码设计和SVM框约束。使用这些值的所有组合：

For all models, store the 5-fold cross-validated misclassification rates.

coding = {'onevsone''onevsall'}; boxconstraint = logspace(-1,2,3); cvLoss = nan(numel(coding),numel(boxconstraint));预付款的百分比fori = 1:numel(coding)forj = 1:numel(boxconstraint) t = templateSVM('BoxConstraint',boxconstraint(j),'标准化'，真的）;cvmdl = fitcecoc（x（idxtrn，:)，y（idxtrn），'Learners',t,'KFold'，5，。。。'Coding',coding{i}); cvLoss(i,j) = kfoldLoss(CVMdl); fprintf('cvloss =模型使用％s编码和框约束=％f \ n',。。。cvloss（i，j），编码{i}，boxconstraint（j））endend

cvLoss = 0.052083 for model using onevsone coding and box constraint=0.100000 cvLoss = 0.055000 for model using onevsone coding and box constraint=3.162278 cvLoss = 0.050000 for model using onevsone coding and box constraint=100.000000 cvLoss = 0.116667 for model using onevsall coding and box constraint=0.100000 cvLoss = 0.123750 for model using onevsall coding and box constraint=3.162278 cvLoss = 0.125000 for model using onevsall coding and box constraint=100.000000

确定hyperparameter指数收益率the minimal misclassification rate. Train an ECOC model using the training data. Standardize the training data and supply the observed, optimal hyperparameter combination.

mincvloss = min（cvloss（:)）

minCVLoss = 0.0500

linIdx = find(cvLoss == minCVLoss); [bestI,bestJ] = ind2sub(size(cvLoss),linIdx); bestCoding = coding{bestI}

bestCoding = 'onevsone'

bestboxconstraint = boxconstraint（bestj）

BestboxConstraint = 100.

t = templateSVM('BoxConstraint',bestBoxConstraint,'标准化'，真的）;Mdl = fitcecoc(X(idxTrn,:),Y(idxTrn),'Learners',t,'Coding',bestCoding);

Construct a confusion matrix for the test set images.

testimages = x（iDxtest，:);testLabels =预测（MDL，Testimages）;confusionmatrix = confusionchart（y（iDxtest），testlabels）;

Diagonal and off-diagonal elements correspond to correctly and incorrectly classified observations, respectively.Mdlseems to correctly classify most images.

If you are satisfied with the performance ofMdl, then you can proceed to generate code for prediction. Otherwise, you can continue adjusting hyperparameters. For example, you can try training the SVM learners using different kernel functions.

分类模型保存到磁盘

Mdlis a predictive classification model, but you must prepare it for code generation. SaveMdlto your present working directory usingsaveLearnerForCoder。

saveLearnerForCoder(Mdl,'digitimagesecoc')

saveLearnerForCodercompactsMdl, converts it to a structure array, and saves it in the MAT-fileDigitImagesECOC.mat。

Define Prediction Function for Code Generation

Define an entry-point function namedpredictDigitECOC.mthat does the following:

typepredictDigitECOC.m% Display contents of predictDigitECOC.m file

function label = predictDigitECOC(X) %#codegen %PREDICTDIGITECOC Classify digit in image using ECOC Model % PREDICTDIGITECOC classifies the 28-by-28 images in the rows of X using % the compact ECOC model in the file DigitImagesECOC.mat, and then % returns class labels in label. CompactMdl = loadLearnerForCoder('DigitImagesECOC.mat'); label = predict(CompactMdl,X); end

Note:If you click the button located in the upper-right section of this page and open this example in MATLAB, then MATLAB opens the example folder. This folder includes the entry-point function file.

Verify that the prediction function returns the same test set labels aspredict。

pfLabels = predictDigitECOC(testImages); verifyPF = isequal(pfLabels,testLabels)

verifyPF =logical1

isequal返回逻辑1（true), which means all the inputs are equal. ThepredictDigitECOCyields the expected results.

确定要执行生成的代码的环境

Generated code can run:

This example generates a MEX file to be run in the MATLAB environment. Generating such a MEX file allows you to test the generated code using MATLAB tools before deploying the function outside the MATLAB environment. In the MEX function, you can include code for verification, but not for code generation, by declaring the commands as extrinsic usingcoder.extrinsic(MATLAB Coder)。Extrinsic commands can include functions that do not have code generation support. All extrinsic commands in the MEX function run in MATLAB, butcodegen不会为它们生成代码。

If you plan to deploy the code outside the MATLAB environment, then you must generate a standalone executable. One way to specify your compiler choice is by using the-configoption ofcodegen。For example, to generate a static C executable, specify-config:exe你打电话的时候codegen。For more details on setting code generation options, see the-configoption ofcodegen(MATLAB Coder)。

Compile MATLAB Function to MEX File

CompilepredictDigitECOC.m使用MEX文件codegen。Specify these options:

codegenpredictDigitECOC-report-args{testImages}

Code generation successful: View report

codegensuccessfully generated the code for the prediction function. You can view the report by clicking theView reportlink or by enteringopen('codegen/mex/predictDigitECOC/html/report.mldatx')在命令窗口中。如果代码生成不成功，那么报告可以帮助您调试。

codegencreates the directorypwd/codegen/mex/predictDigitECOC, wherepwdis your present working directory. In the child directory,codegengenerates, among other things, the MEX-filepredictDigitECOC_mex.mexw64。

Verify that the MEX file returns the same labels aspredict。

mexLabels = predictDigitECOC_mex(testImages); verifyMEX = isequal(mexLabels,testLabels)

验证ex =logical1

isequal返回逻辑1（true), meaning that the MEX-file yields the expected results.