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
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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)
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