How to design LSTM-CNN on deep network designer?

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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.

Risposta accettata

Divya Gaddipati
Divya Gaddipati il 10 Mar 2021
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.
  1 Commento
NurAlisa Ali
NurAlisa Ali il 29 Apr 2021
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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Più risposte (2)

Dreaman
Dreaman il 28 Mar 2021
i have the same problem too, have u solved this problem?
  2 Commenti
NurAlisa Ali
NurAlisa Ali il 29 Apr 2021
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 il 9 Giu 2022
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 il 5 Nov 2022
% 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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