我怎么能修改的第一层pre-trained款

                             解压缩(“MerchData.zip”);
                            
                             imd = imageDatastore (“MerchData”,“IncludeSubfolders”,真的,“LabelSource”,“foldernames”);
                            
                             [imdsTrain, imdsTest] = splitEachLabel (imd, 0.7,“随机”);
                            
                             numTrainImages =元素个数(imdsTrain.Labels);
                            
                             idx = randperm (numTrainImages 16);
                            
                             图
                            
                             为i = 1:16
                            
                             次要情节(4 4,我)
                            
                             I = readimage (imdsTrain idx (I));
                            
                             imshow(我)
                            
                             结束
                            
                             网= resnet18;
                            
                             analyzeNetwork(净)
                            
                             inputSize = net.Layers (1) .InputSize;
                            
                             %创建图层,将取代输入层
                            
                             %你的图像的大小必须更大或等于输入的大小。
                            
                             [28 28层= imageInputLayer (3),“名字”,“输入”,“归一化”,“zscore”);
                            
                             %正常化和移动数据的意思
                            
                             layer.Mean = lgraph.Layers (1, 1) .Mean;
                            
                             layer.StandardDeviation = lgraph。层(1,1).StandardDeviation;
                            
                             % %移动网络层图
                            
                             lgraph = layerGraph(净);
                            
                             newlgraph = replaceLayer (lgraph,“数据”层);
                            
                             %再次组装工作款
                            
                             网= assembleNetwork (newlgraph);
                            
                             analyzeNetwork(净)
                            
                             augimdsTrain = augmentedImageDatastore (inputSize (1:2), imdsTrain);
                            
                             augimdsTest = augmentedImageDatastore (inputSize (1:2), imdsTest);
                            
                             层=“pool5”;
                            
                             featuresTrain =激活(净、augimdsTrain层,“OutputAs”,“渠道”);
                            
                             featuresTrain =挤压(featuresTrain);
                            
                             featuresTrain = featuresTrain ';
                            
                             featuresTest =激活(净、augimdsTest层,“OutputAs”,“渠道”);
                            
                             featuresTest =挤压(featuresTest);
                            
                             featuresTest = featuresTest ';
                            
                             谁featuresTrain
                            
                             analyzeNetwork(净)
                            
                             YTrain = imdsTrain.Labels;
                            
                             欧美= imdsTest.Labels;
                            
                             分类器= fitcecoc (featuresTrain YTrain);
                            
                             YPred =预测(分类器,featuresTest);
                            
                             idx = [1 5 10 15];
                            
                             图
                            
                             为i = 1:元素个数(idx)
                            
                             次要情节(2,2,我)
                            
                             I = readimage (imdsTest idx (I));
                            
                             标签= YPred (idx (i));
                            
                             imshow(我)
                            
                             标题(char(标签)
                            
                             结束
                            
                             精度=意味着(YPred = =次)