How to use different dimensions for neural network?

I'm trying to write a neural network that calculates the cross product of 2 vectors in which elements are between -1 and 1.

So as Xtrain it gets an array with 3x2 matrices or basically 2 vectors and as Ytrain it's an array of vectors. So dimensions are 3,2,1000 and 3,1000. Here is my code:

Xtrain = zeros(3,2,1000);
Ytrain = zeros(3,1000);
for c = 1:1000
    v1 = -1 + (1+1)*rand(3,1);
    v2 = -1 + (1+1)*rand(3,1);
    C = cross(v1,v2);
    m = [v1, v2];
    Xtrain(:,:,c) = m;
    Ytrain(:,c) = C;
end

net = feedforwardnet([3 2]);
net.layers{1}.transferFcn = 'tansig';
net.layers{2}.transferFcn = 'purelin';
net = train(net, Xtrain, Ytrain);

However Matlab gives 'Inputs X is not two-dimensional.' error. I wonder what is the best solution here? One of my ideas is to convert Xtrain into an array of cells, so the cell would consist of 2 vectors but as far as I understand it's an antipattern.

Answer 1

[Sam Chak]

@d

https://www.mathworks.com/help/deeplearning/ref/network.train.html

Documentation says that Network inputs must be specified as an InputSize-by-BatchSize matrix. Is this approach acceptable?

Xtrain = zeros(6,5);
Ytrain = zeros(3,5);
for c = 1:5
    v1 = -1 + (1+1)*rand(3,1);
    v2 = -1 + (1+1)*rand(3,1);
    C = cross(v1,v2);
    m = [v1; v2];
    Xtrain(:,c) = m;
    Ytrain(:,c) = C;
end

net = feedforwardnet([3 2]);
net.layers{1}.transferFcn = 'tansig';
net.layers{2}.transferFcn = 'purelin';
net = train(net, Xtrain, Ytrain);

[d]

Thank you!