How to design LSTM-CNN on deep network designer?
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NurAlisa Ali on 7 Mar 2021
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.
Divya Gaddipati on 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.
More Answers (2)
Dreaman on 28 Mar 2021
i have the same problem too, have u solved this problem?
Manoj Devaraju on 9 Jun 2022
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?
H W on 5 Nov 2022
% Load data
[XTrain,YTrain] = japaneseVowelsTrainData;
% Define layers
layers = [ sequenceInputLayer(12,'Normalization','none', 'MinLength', 9);
lstmLayer(100, 'OutputMode', 'last')
options = trainingOptions('adam', ...
% Train network
net = trainNetwork(XTrain,YTrain,layers,options);
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