multidimensional array and deep learning

Hi,

I am trying to create a simple deep learning network but it 'appears' that they are not compatible with multidimensional arrays. I have a data-set of (30,30,97000) of positive example and (30,30,200000) negative examples. the names and pos/neg tags are in separate arrays. Need some direction on how to get the data into the prop format for deep learning.

Do i need to write a loop and save all of the files individually so i can build a data store afterwards? seems time inefficient...

quick mock-up example

good=rand(30,30,100); bad=rand(30,30,200);
g_label=(1:1:100); g_label(2,:)=1; 
b_label=(101:1:300); b_label(2,:)=1;

Answer 1

[Jason Dortch]

I obtained help from tech support. The Deep learning tools will take a 4-dimensional array. using the permute function, I added one dimension so my data is arranged like a grayscale image - (width, height, channel, number of images).

Good=rand(30,30,100);
Good=permute(Good, [1 2 4 3]);

Size(Good)
30 30 1 100

Hope that helps someone, Jason