MATLAB: Sequence to Sequence Classification with Deep Learning CNN+LSTM

Question

I was looking through the possible implementation of sequence classification using deep-learning.

There are pllenty of example of LSTM/BILSTM implementations

A) https://it.mathworks.com/help/deeplearning/ug/sequence-to-sequence-classification-using-deep-learning.html

and 1D-Convolutional implementations of the problem.

B) https://it.mathworks.com/help/deeplearning/ug/sequence-to-sequence-classification-using-1-d-convolutions.html

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)?

Answer 1

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.