Custom regression layer in Deep Learning Toolbox

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Hi.
I have a quick question about "Custom Regression Output Layer."_ https://www.mathworks.com/help/nnet/ug/define-custom-regression-output-layer.html
My goal is similar to this: https://www.mathworks.com/help/nnet/examples/train-a-convolutional-neural-network-for-regression.html
My understanding is that, in the first step, a batch of images goes through the network and compute the loss by learning a function of the forwardloss. However, I realized that MATLAB runs a function of the backwardloss first.
Could you explain why the backwardloss runs first?
Chulmin
  2 Comments
NM
NM on 18 May 2018
Hi Chulmin, I am using the same example to forecast electricity demand. However, I couldn't translate the example to make it usable with my data as my data doesn't contain images. Can you please help me out? My data: Xtrain (9x800) double type, Ytrain (1x800)
Chul Min Yeum
Chul Min Yeum on 22 May 2018
Could you more elaborate your data and purpose? Cnn is designed for image data. You can simply use a neural network toolbox for your problem.

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