How to design LSTM-CNN on deep network designer?

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NurAlisa Ali
NurAlisa Ali 2021-3-7
回答： H W 2022-11-5
Hello,
My project is on classification of ECG/EEG signals using deep learning. I have design based on sequence on LSTM layer. Now i want to design hybrid LSTM-CNN on deep network designer which i have problem with connection between LSTM and Convolutional layer. I used Sequencefolding layer (suggested by deep network designer) after LSTM and connect to Convolutionallayer2d. The problem is Sequencefolding layer have two output (1. output, 2. minibatchsize) , which i don't now where to connect this minibatchsize connection. Can somebody expert give me advice on this? Really appreciate on any advice.
Thanks in advance sir.

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采纳的回答

Divya Gaddipati
Divya Gaddipati 2021-3-10
You have to use a sequenceUnfoldingLayer that takes two inputs, feature map and the miniBatchSize from the corresponding sequenceLayer. You can refer to this example for more information.
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NurAlisa Ali
NurAlisa Ali 2021-4-29
Thank you very much for this sir. From the example given, it is for hybrid CNN-LSTM, what i'm try to design is LSTM-CNN....

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更多回答（2 个）

Dreaman
Dreaman 2021-3-28
i have the same problem too, have u solved this problem?
  2 个评论
NurAlisa Ali
NurAlisa Ali 2021-4-29
Yeah i have try CNN-LSTM, but the input length must be not too long, otherwise will get out of memory even 32GB ram.
Manoj Devaraju
Manoj Devaraju 2022-6-9
Hello Ali,
Evn I would like to apply CNN-LSTM network for the image data set classification problem. But unfortunately i am struggling to apply, can you please give me some insight, how can it be done?

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H W
H W 2022-11-5
% Load data
[XTrain,YTrain] = japaneseVowelsTrainData;
% Define layers
layers = [ sequenceInputLayer(12,'Normalization','none', 'MinLength', 9);
convolution1dLayer(3, 16)
batchNormalizationLayer()
reluLayer()
maxPooling1dLayer(2)
convolution1dLayer(5, 32)
batchNormalizationLayer()
reluLayer()
averagePooling1dLayer(2)
lstmLayer(100, 'OutputMode', 'last')
fullyConnectedLayer(9)
softmaxLayer()
classificationLayer()];
options = trainingOptions('adam', ...
'MaxEpochs',10, ...
'MiniBatchSize',27, ...
'SequenceLength','longest');
% Train network
net = trainNetwork(XTrain,YTrain,layers,options);

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