今天给大家分享基于小波分析的squeezenet网络时间序列分类MATLAB实战。需要了解更多算法代码的，可以点击文章左下角的阅读全文，进行获取哦~需要了解智能算法、机器学习、深度学习和信号处理相关理论的可以后台私信哦，下一期分享的内容就是你想了解的内容~
本次实战使用连续小波变换(CWT)和squeezenet网络对人类心电图(ECG)信号进行分类。为加快训练速度，采用squeezenet网络进行迁移学习。对图像识别进行了预训练，以基于时频表示对ECG波形进行分类。本次使用的数据可从PhysioNet公开获得。
%%基于小波分析的squeezenet网络时间序列分类clcclear%% 数据处理unzip(fullfile(tempdir,"physionet_ECG_data-main.zip"),tempdir)unzip(fullfile(tempdir,"physionet_ECG_data-main","ECGData.zip"), ...fullfile(tempdir,"physionet_ECG_data-main"))load(fullfile(tempdir,"physionet_ECG_data-main","ECGData.mat"))parentDir = tempdir;dataDir = "data";helperCreateECGDirectories(ECGData,parentDir,dataDir)helperPlotReps(ECGData)%%Fs = 128;fb = cwtfilterbank(SignalLength=1000, ...SamplingFrequency=Fs, ...VoicesPerOctave=12);sig = ECGData.Data(1,1:1000);[cfs,frq] = wt(fb,sig);t = (0:999)/Fs;figurepcolor(t,frq,abs(cfs))set(gca,"yscale","log")shading interpaxis tighttitle("Scalogram")xlabel("Time (s)")ylabel("Frequency (Hz)")%%helperCreateRGBfromTF(ECGData,parentDir,dataDir)allImages = imageDatastore(fullfile(parentDir,dataDir), ..."IncludeSubfolders",true, ..."LabelSource","foldernames");[imgsTrain,imgsValidation] = splitEachLabel(allImages,0.8,"randomized");disp("Number of training images: "+num2str(numel(imgsTrain.Files)))disp("Number of validation images: "+num2str(numel(imgsValidation.Files)))%% 下载squeezenetsqz = squeezenet;lgraphSqz = layerGraph(sqz);disp("Number of Layers: "+num2str(numel(lgraphSqz.Layers)))lgraphSqz.Layers(1)lgraphSqz.Layers(end-5:end)tmpLayer = lgraphSqz.Layers(end-5);newDropoutLayer = dropoutLayer(0.6,"Name","new_dropout");%% 修改squeezenet最后5层架构，实现模型迁移lgraphSqz = replaceLayer(lgraphSqz,tmpLayer.Name,newDropoutLayer);numClasses = numel(categories(imgsTrain.Labels));tmpLayer = lgraphSqz.Layers(end-4);newLearnableLayer = convolution2dLayer(1,numClasses, ..."Name","new_conv", ..."WeightLearnRateFactor",10, ..."BiasLearnRateFactor",10);lgraphSqz = replaceLayer(lgraphSqz,tmpLayer.Name,newLearnableLayer);tmpLayer = lgraphSqz.Layers(end);newClassLayer = classificationLayer("Name","new_classoutput");lgraphSqz = replaceLayer(lgraphSqz,tmpLayer.Name,newClassLayer);lgraphSqz.Layers(end-5:end)%% 数据增强augimgsTrain = augmentedImageDatastore([227 227],imgsTrain);augimgsValidation = augmentedImageDatastore([227 227],imgsValidation);%% 设置网络参数ilr = 3e-4;miniBatchSize = 10;maxEpochs = 15;valFreq = floor(numel(augimgsTrain.Files)/miniBatchSize);opts = trainingOptions("sgdm", ...MiniBatchSize=miniBatchSize, ...MaxEpochs=maxEpochs, ...InitialLearnRate=ilr, ...ValidationData=augimgsValidation, ...ValidationFrequency=valFreq, ...Verbose=1, ...Plots="training-progress");trainedSN = trainNetwork(augimgsTrain,lgraphSqz,opts);trainedSN.Layers(end)[YPred,probs] = classify(trainedSN,augimgsValidation);accuracy = mean(YPred==imgsValidation.Labels);disp("SqueezeNet Accuracy: "+num2str(100*accuracy)+"%")
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今天的分享就到这里了，后续想了解智能算法、机器学习、深度学习和信号处理相关理论的可以后台私信哦~希望大家多多转发点赞加收藏，你们的支持就是我源源不断的创作动力！
作 者 | 华 夏
编 辑 | 华 夏
校 对 | 华 夏
