Sequence CNN with different input and output size

I'm trying to train a Regression Sequence CNN with the following properties:

1. All training input sequences have length L
2. All training output sequences have length LOut with LOut <= L

By default MATLAB requires that L = LOut and the training is really good when L=LOut. Then I was trying to fix the case LOut<L by filling the output sequence of training with zeros but the training does not converge. Then I was trying to construct a Custom Regression Layer where the loss function only considers the values between 1 to LOut but again there's no training convergence. Then I tried to construct a Custom Fully Connected Layer but the Checking Layer Process fails, I think that the Checking process fails because matlab does 2 checking stages where the size of the sequence input varies between 1 and 3 (I checked it with the debugger) and since the learnable weights have a fixed sice the Checking process fails in the multiplication and sum operations .

Any sugestion?

Adjoint the predict function of each Custom layer:

Regression Layer

 function loss = forwardLoss(layer, Y, T)            
            LOut_ = layer.LOut;            
            meanAbsoluteError = sum(abs(Y(:,:,1:LOut_)-T(:,:,1:LOut_,R)),3)/LOut_;
    
            % Take mean over mini-batch.
            N = size(Y,4);
            loss = sum(meanAbsoluteError)/N;            
 end 

Fully Connected Layer

function [Z] = predict(layer,X)
            [l,m,n]=size(X); %m is ne number of features and n is the length of the sequences. l is usually the minibatch size.
            mBS = size(X,2);
            LOut_ = layer.LOut;
            W_=layer.W;
            b_= layer.b;
            Z = zeros(l,m,LOut_);
            for i = 1:l
                Z(i,:,:) = (W_*squeeze(X(i,:,:))'+b_)';
            end            
        end

Answer 1

[Raynier Suresh]

Hi, You can try an encoder decoder model to achieve this. The below example explains you about how to set an encoder decoder model for sequence to sequence translation.

https://www.mathworks.com/help/deeplearning/ug/sequence-to-sequence-translation-using-attention.html