If you look closely at the documentation for "trainNetwork", you can see that the input data can be a 4D Numeric Matrix, and the output can be a 2D Numeric matrix for regression. This documentation is linked below:
This input format is required because the input layer more or less expects to treat each as an h by w by c image, where h is height, w is width, and c is the number of channels in the image. This requires you to initialize extra dimensions as a placeholder in the input. Note that you must do this for your training and validation data. To do this, you can transform 'X' as below:
Xnew = reshape(x, [size(x,1),1,1,size(x,2)]);
Similarly, you must transform the output training data to be a Number of Observations by Number of Response Variables matrix. This can be done by simply setting this to the transpose of 'X'. Again, this must be done for both training and validation data.
