% lstmLayer(numhidden_units1,'Outputmode','sequence') %学习层设置(cell层) lstmLayer(numhidden_units1,'Outputmode','sequence','name','hidden1') %隐藏层1 dropoutLayer(0.3,'name','dropout_1') %隐藏层1权重丢失率,防止过拟合 lstmLayer(numhidden_units2,'Outputmode','last','name','hidden...
1.NumHiddenUnits—隐藏单元的数量 2.OutputMode—输出格式 'sequence' – 输出完整的序列。 'last' – 输出序列的最后一个时间步。 3.InputSize—输入大小 输入大小,指定为正整数或'auto'。如果InputSize是'auto',则软件会在训练时自动分配输入大小。 1. 2. 3. 4. 5. 6. 激活设置 1.StateActivationFunct...
%%建立神经网络层 layers=[sequenceInputLayer(1,"Name","input")%输入特征数lstmLayer(20,"Name","lstm",'OutputMode','last')%隐藏单元dropoutLayer(0.1,"Name","drop")%遗忘门fullyConnectedLayer(duobuyuce,"Name","fc")%全连接层regressionLayer("Name","regressionputput")];%回归输出%%定义训练参数 ...
%% 建立神经网络层layers = [sequenceInputLayer(1,"Name","input") % 输入特征数lstmLayer(20,"Name","lstm",'OutputMode','last') % 隐藏单元dropoutLayer(0.1,"Name","drop") % 遗忘门fullyConnectedLayer(duobuyuce,"Name","fc") % 全连接层regressionLayer("Name","regressionputput")]; % 回归...
(X, 2); % 特征数量 20numHiddenUnits = 100; % LSTM隐藏层单元数 21numClasses = 1; % 预测任务,假设为回归问题则为1 22 23layers = [ 24 sequenceInputLayer(numFeatures) 25 lstmLayer(numHiddenUnits, 'OutputMode', 'last') 26 fullyConnectedLayer(numClasses) 27 regressionLayer 28]; 29 30%...
lstmLayer(numHiddenUnits,'OutputMode','last')。 fullyConnectedLayer(numClasses)。 softmaxLayer. classificationLayer]; % 指定训练选项。 options = trainingOptions('adam', ... 'MaxEpochs',50, ... 'GradientThreshold',1, ... 'InitialLearnRate',0.01, ... 'LearnRateSchedule','piecewise', ......
lstmLayer(numHiddenUnits,'OutputMode','last') lstmLayer(numHiddenUnits-30) lstmLayer(numHiddenUnits-60) fullyConnectedLayer(1) regressionLayer] options = trainingOptions('adam',... 'InitialLearnRate',1e-3,...% 学习率 'MiniBatchSize', 8, ... ...
{i, 1} = P_test( :, :, 1, i);end%% 创建网络layers = [ ... sequenceInputLayer(12) % 输入层 lstmLayer(6, 'OutputMode', 'last') % LSTM层 reluLayer % Relu激活层 fullyConnectedLayer(4) % 全连接层 softmaxLayer % 分类层 classificationLayer];%% 参数设置options = trainingOptions('...
[inputSize,~]=size(scat_features_train{1});YTrain=categorical(trainLabels);numHiddenUnits=100;%隐含层神经元数numClasses=numel(unique(YTrain));%类别数量maxEpochs=125;%训练次数miniBatchSize=1000;%最小批处理数量layers=[...sequenceInputLayer(inputSize)lstmLayer(numHiddenUnits,'OutputMode','last'...
The LSTM long and short time memory network adds the unit state on the basis of RNN, that is, at a certain moment, the input of LSTM has three inputs: current time network input value x_t, the output of LSTM at the previous time h_(t-1), and unit state at last time c_(t-1...