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"Example: Define Custom Deep Learning Layer with Learnable Parameters" doesn't work

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Hellow, I wrote this question due to getting error for abstract method inheritance.
In this example, The written class file in the part named 'Completed Layer' is just used to test for the below 'check validity of Layer Using checkLayer' part:
layer = preluLayer(20,'prelu');
validInputSize = [24 24 20];
checkLayer(layer,validInputSize,'ObservationDimension',4)
But, in 'layer = preluLayer(20, 'prelu'); part, the error message was created: "Creating an instance of the Abstract class is not allowd"
Actually, I know the nnet.layer.Layer object has 2 abstract method named as 'predict' and 'backward', and this example just define 'predict' so the error will be from undefining 'backward'. Even though, I just had followed the example but I got error.
Could you please some tips to resolve this problem?

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Accepted Answer

Asvin Kumar
Asvin Kumar on 23 Dec 2019
Thanks for pointing this out. Check out the complete file by running the following command:
edit(fullfile(matlabroot,'examples','nnet','main','preluLayer.m'))
This file contains the implementation for the backward function too.
As mentioned in the example, it is not required to implement a backward function for layers whose predict/forward functions use only dlarray supported functions. In this example, the predict uses layer.Alpha which is created using randn. The randn function has a limitation for dlarray support. It works when used along with the ‘like’ argument.

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