layer = sequenceInputLayer(12) layer = SequenceInputLayer with properties: Name: '' InputSize: 12 MinLength: 1 SplitComplexInputs: 0 Hyperparameters Normalization: 'none' NormalizationDimension: 'auto' Include
numFeatures = 1;%输入特征维数 numResponses = 1;%输出响应维数 numHiddenUnits = 100;%隐层神经元...
为了补偿训练数据中不同的时间序列,使用 Padding="causal" 作为 convolution1dLayer 中的名称-值对输入参数。 numFeatures = size(XTrain{1},1); numHiddenUnits = 100; numResponses = 1; layers = [ sequenceInputLayer(numFeatures) convolution1dLayer(5,32,Padding="causal") batchNormalizationLayer() ...
%请使用与序列到标签分类相同的架构,但将 LSTM 层的输出模式设置为'sequence'。 numFeatures = 12; numHiddenUnits = 100; numClasses = 9; layers = [ ... sequenceInputLayer(numFeatures) lstmLayer(numHiddenUnits,'OutputMode','sequence') fullyConnectedLayer(numClasses) softmaxLayer classificationLayer]...
numResponses = 1; FiltZise = 10; % 创建"CNN-LSTM"模型 layers = [... % 输入特征 sequenceInputLayer([numFeatures 1 1],'Name','input') sequenceFoldingLayer('Name','fold') % CNN特征提取 convolution2dLayer([FiltZise 1],32,'Padding','same','WeightsInitializer','he','Name','conv','...
sequenceInputLayer(numFeatures)%输入层,参数是输入特征维数 lstmLayer(Tuna1(1,1))%lstm层,如果想要构建多层lstm,改几个参数就行了 fullyConnectedLayer(numResponses)%全连接层,也就是输出的维数 regressionLayer];%该参数说明是在进行回归问题,而不是分类问题 ...
numFeatures=1;%输入节点 numResponses=1;%输出节点 numHiddenUnits=500;%隐含层神经元节点数%构建LSTM网络 layers=[sequenceInputLayer(numFeatures)lstmLayer(numHiddenUnits)%lstm函数dropoutLayer(0.2)%丢弃层概率reluLayer('name','relu')%激励函数RELUfullyConnectedLayer(numResponses)regressionLayer];XTrain=XTra...
sequenceInputLayer要求时间维度沿第二维度。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 featuresTrain = permute(featuresTrain,[2,1,3]); featuresTrain = squeeze(num2cell(featuresTrain,[1,2])); numSignals = numel(featuresTrain) numSignals = 2400 [numFeatures,numHopsPerSequence] = size(...
numFilters = 64; filterSize = 5; dropoutFactor = 0.005; numBlocks = 4; net = dlnetwork; layer = sequenceInputLayer(numFeatures,Normalization="rescale-symmetric",Name="input"); net = addLayers(net,layer); outputName = layer.Name; for i = 1:numBlocks dilationFactor = 2^(i-1); l...
5x1 Layer arraywithlayers:1''SequenceInputSequenceinputwith12dimensions2''BiLSTM BiLSTMwith100hidden units3''Fully Connected9fully connected layer4''Softmax softmax5''Classification Output crossentropyex 现在,指定训练选项。将优化器指定为'adam',将梯度阈值指定为1,将最大历元数指定为100。要减少小批量...