So I’m quite new to the world of Artificial Intelligence. I’m currently working on building a classification algorithm for raw data. This model will have 4 inputs, each a rank-1 tensor, but of different sizes. I could potentially make them all the same size if needed, but I would like to avoid this if possible. The output of the model would be a group number prediction based on softmax activation. My question is would it be better in terms of model accuracy to use the 4 arrays as separate inputs to a Functional model, or should I manipulate the arrays to all be the same size and create a Sequential model with one, rank-2 tensor as its input?
I apologize in advance if anything is poorly worded, or if there is necessary information missing. Please let me know if I can clarify anything.