二、代码实战
clcclear%%下载训练集和测试集s = load("HumanActivityTrain.mat");XTrain = s.XTrain;TTrain = s.YTrain;numObservations = numel(XTrain);classes = categories(TTrain{1});numClasses = numel(classes);numFeatures = size(s.XTrain{1},1);s1 = load("HumanActivityTest.mat");XTest = s1.XTest;TTest = s1.YTest;%% 搭建TCN网络numFilters = 64;filterSize = 5;dropoutFactor = 0.005;numBlocks = 4;layer = sequenceInputLayer(numFeatures,Normalization="rescale-symmetric",Name="input");lgraph = layerGraph(layer);outputName = layer.Name;for i = 1:numBlocksdilationFactor = 2^(i-1);layers = [convolution1dLayer(filterSize,numFilters,DilationFactor=dilationFactor,Padding="causal",Name="conv1_"+i)layerNormalizationLayerspatialDropoutLayer(dropoutFactor)convolution1dLayer(filterSize,numFilters,DilationFactor=dilationFactor,Padding="causal")layerNormalizationLayerreluLayerspatialDropoutLayer(dropoutFactor)additionLayer(2,Name="add_"+i)];% Add and connect layers.lgraph = addLayers(lgraph,layers);lgraph = connectLayers(lgraph,outputName,"conv1_"+i);% Skip connection.if i == 1% Include convolution in first skip connection.layer = convolution1dLayer(1,numFilters,Name="convSkip");lgraph = addLayers(lgraph,layer);lgraph = connectLayers(lgraph,outputName,"convSkip");lgraph = connectLayers(lgraph,"convSkip","add_" + i + "/in2");elselgraph = connectLayers(lgraph,outputName,"add_" + i + "/in2");end% Update layer output name.outputName = "add_" + i;endlayers = [fullyConnectedLayer(numClasses,Name="fc")softmaxLayerclassificationLayer];lgraph = addLayers(lgraph,layers);lgraph = connectLayers(lgraph,outputName,"fc");%% 画网络结构figureplot(lgraph)title("Temporal Convolutional Network")%% 设置网络参数options = trainingOptions("adam", ...MaxEpochs=60, ...miniBatchSize=1, ...Plots="training-progress", ...Verbose=0);%% 训练网络net = trainNetwork(XTrain,TTrain,lgraph,options);%% 测试网络YPred = classify(net,XTest);figureconfusionchart(TTest{1},YPred{1})accuracy = mean(YPred{1} == TTest{1})
仿真结果：
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作 者 | 华 夏
编 辑 | 华 夏
校 对 | 华 夏
