Create a datastore from a table
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I'm getting up to speed with the Deep Learning Toolbox. The Datastore concept has several benefits. The obvious one is that it manages data that is too big to fit in memory. But it has other advantages, like the "splitEachLabel" function, which divides the data preserving the proportion of each label.
I have a table with my predictor and response variables. I'd like to be able to convert it to a (in-memory) datastore. The function arrayDatastore would seem to be the way to go, but it seems to make a datastore only of a homogeneous array, for example my predictors. I can't figure out how to combine the predictors and responses (as Labels) so that I can hand the one datastore to trainNetwork.
What am I missing?
Jeremy Hughes am 30 Nov. 2021
I had no issue with arrayDatastore taking a table. Could you share some sample code with the errors or problems you're seeing?
A = array2table(rand(5))
ds = arrayDatastore(A,"OutputType","same")
Each read call returns a one row table. Maybe not what you're lookinf for, but it's "working" for some definition.
BTW: If you don't supply the OutputType, the result is a cell, but it still reads the data, it just wraps the contents in a cell.