Sequence to Sequence Classification with Deep Learning CNN+LSTM
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Mirko Job
am 22 Mär. 2020
Kommentiert: Srivardhan Gadila
am 25 Mär. 2020
I was looking through the possible implementation of sequence classification using deep-learning.
There are pllenty of example of LSTM/BILSTM implementations
and 1D-Convolutional implementations of the problem.
My question is there is a way to combine the two solutions?
If for the first one the building of the net seems pretty immediate by stacking series of custom layers:
layers = [ ...
sequenceInputLayer(numFeatures)
lstmLayer(numHiddenUnits,'OutputMode','sequence')
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer];
The convolution implementation seems indeed more complex, as it directly defines the various computational blocks.
Can i use a pre-defined convolution2Dlayer in the layers structure like in A) or do i have to go deeply in coding as described in B)?
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Srivardhan Gadila
am 25 Mär. 2020
I think you can use the convolution2Dlayer with appropriate input arguments but make sure you use the sequenceFoldingLayer, sequenceUnfoldingLayer wherever necessary. Also refer to List of Deep Learning Layers.
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Srivardhan Gadila
am 25 Mär. 2020
Refer to the following MATLAB Answer: CNN code and Sequence Input Error
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