Hello, I am trying to train a deep neural network that classifies based on the data present in an [X by Y] grayscale image. However, prior to this, 5 regression values are being calculated from the image as well. I am trying to make a network that takes both the image, and the 5 regression values as inputs for each output.
I currently am using a table where the first column is the path to each image, and the second column is the classifier. However, I believe I learned that the trainNetwork function can take an arbitrary number of inputs paired with each output.
However, how should I format the network when the first column is an image file, and the next 5 are regression values, followed finally by the classifier? The image input layer can only take the image of course, so how do I also tell the network to input the 5 regression values?
I would really like to avoid breaking down the image into a sequence of values followed by the regression values for use in a sequenceInputLyaer - the images are large and there are hundreds of thousands of them.
Thanks for the advice!