Deep Learning Toolbox - fullyConnectedLayer output dimension

Problem

I am new to the Deep Learning Toolbox and I am trying to create a custom layer.

This layer of mine is supposed to come after a fully connected layer but I seem to misunderstand the expected output dimension of such layer.

As I thought, if I create a fullyConnectedLayer with output dimension of, say, 3:

fullyConnectedLayer(3)

The output should be a vector of size [3 1] (or more accuratley, a cell with this vector in first position).

Example

To clarify my problem lets consider a simple network. My train data is trainX (of size [1000 12], 1000 observations of 12-element vectors) and train "tags" are trainS (of size [1000 6], 1000 observations of 6-element vectors). The test data is dataX and tags are dataS (same dimensions as the train data).

The network is defined as follows:

%% Prepare Data
VALIDATION_PERCENT = 0.1;
load('data.mat');

validationX = trainX(1:floor(VALIDATION_PERCENT * length(trainX)), :);
validationS = trainS(1:floor(VALIDATION_PERCENT * length(trainS)), :);

trainX = trainX(floor(VALIDATION_PERCENT * length(trainX))+1:end, :);
trainS = trainS(floor(VALIDATION_PERCENT * length(trainS))+1:end, :);
%% Define Network
layers = [ ...
    sequenceInputLayer(12)
    fullyConnectedLayer(3)
    myLayer()
    regressionLayer
    ];

options = trainingOptions('sgdm', ...
    'ValidationData', {num2cell(validationX', 1), validationS});
%% Train Network
[trainedNet, traininfo] = trainNetwork(num2cell(trainX', 1), trainS, layers, options);

Were myLayer is a custom layer that does nothing but printing the dimension of its input:

classdef myLayer < nnet.layer.Layer
    
    methods
        
        function varargout = predict(~, varargin)
            disp(size(varargin{:}))
            varargout = varargin;
        end
        
        function dLdX = backward(~, ~, ~, dLdZ, ~)
            dLdX = dLdZ;
        end
    
    end
end

The Command Window, as I ran the script above, shows:

     3     3

     3     1

     3     1

     3     3

     3     5

And right after that I get the following error (mainNetwork is the name of the neural network script):

Error using trainNetwork (line 150)

Invalid training data. If all recurrent layers have output mode 'sequence', then regression

responses must be a cell array of numeric sequences, or a single numeric sequence.

Error in mainNetwork (line 22)

[trainedNet, traininfo] = trainNetwork(num2cell(trainX', 1), trainS, layers, options);

From this, three questions arise:

1. Why does the output of the fullyConnectedLayer is not exclusivley of size [3 1]?
2. Why does the output of the fullyConnectedLayer changes its size each iteration?
3. What does the error mean, any why?

Thank you for reading and for your answer!