Deep Learning Toolbox: Inputs and Outputs

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Hi everyone, I have started to transition from my own Matlab ML tool suite into the Deep Learning toolbox, and I have been unable to do the most simple of problems (a vector of elements for an input and a vector of elements for an output). Do you all know how to do this with mulitple hidden layers?
I have tried two different paths, and so far, I have failed royally.
For the 'trainNetwork', I am looking for an input layer for something that isn't a matrix in time sequence or not, but I am able to output a vector of outputs.
For 'train', I have been able to figure out how to define multiple inputs, but I am unable to output more than one value from the final layer.
This is rather dumbfounding, and I hope one of you have figured out how to fix this problem.
The image below may help you all grasp what I am trying to do, and I should note that this will be integrated into an AutoML process to define the network structure, so a GUI based answer won't work.
Matlab 19a with Deep Learning Toolbox, but I could transfer to 19b (I believe) if required.

Accepted Answer

Blake Van Winkle
Blake Van Winkle on 25 Oct 2019
If you are having this issue, I found that it was much easier to just use imageInputLayer but not have it defined as an image format.
For a vector: imageInputLayer([n,1,1])
For a 2d matrix: imageInputLayer([n,m,1])
For a 3d matrix: imageInputLayer([n,m,o])
Thanks for other suggestions...

More Answers (1)

Sai Bhargav Avula
Sai Bhargav Avula on 23 Oct 2019
Hi,
If the network also has multiple inputs and outputs, then you must define the network as a function and train the network using a custom training loop. for more information.
For a detailed understanding on how to it needs to be done refer the following link.
Hope this helps
  1 Comment
Blake Van Winkle
Blake Van Winkle on 25 Oct 2019
To Sai:
While I am assuming the documentation makes sense to some, it is clear as mud to me. You wouldn't happen to have an example handy?
Thanks

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