How to create personalized layers
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Hello everyone
I built a customed regression output layer named mylayer from here https://it.mathworks.com/help/deeplearning/ug/define-custom-regression-output-layer.html and I want to add it to the other layers of the network. I should use trainNetwork but I only found this example of definiton of layers:
layers = [
imageInputLayer([28 28 1])
convolution2dLayer(5,20)
batchNormalizationLayer
reluLayer
fullyConnectedLayer(1)
myLayer('myL')]
My problem however is NOT an image classification, I just want a couple of hidden layers with a number of neurons to be chosen and the usual weight and biases, passing as inputs a some scalar values. How should I define my object layers?
Thanks you in advice for your help!
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Anshika Chaurasia
am 31 Aug. 2020
Hi Fabrizio,
It is my understanding that you have successfully created the custom Regression Ouput Layer – ‘myLayer’. You want to have some hidden layers and ‘myLayer’ in layers array. You could consider following codes:
layers = [
imageInputLayer([28 28 1])
fullyConnectedLayer(20)
reluLayer % optional
fullyConnectedLayer(1)
myLayer('myL')]
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