How to create personalized layers

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Fabrizio Bernardi
Fabrizio Bernardi 2020-8-27
编辑： Fabrizio Bernardi 2020-8-31
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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回答（1 个）

Anshika Chaurasia
Anshika Chaurasia 2020-8-31
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')]
Refer to fullyConnectedLayer documentation for weight and bias properties of fullyConnectedLayer.
  2 个评论
Fabrizio Bernardi
Fabrizio Bernardi 2020-8-31
Thank you for the answer! Is imageInputlayer ok if I don't have to deal with images? Searching I found sequenceInputLayer that could be useful, but trying with a dataset in matlab it gives me this error:
Error using trainNetwork (line 170)
Number of elements must not change. Use [] as one of the size inputs to automatically calculate the appropriate size for that
dimension.
Error in test_prova (line 17)
net = trainNetwork(bodyfatInputs,bodyfatTargets,layers,options);
Caused by:
Error using reshape
Number of elements must not change. Use [] as one of the size inputs to automatically calculate the appropriate size for that
dimension.
This is the code I used ( my customed layer is the layer using mae loss function such as in the example here https://it.mathworks.com/help/deeplearning/ug/define-custom-regression-output-layer.html):
layer = maeRegressionLayer('mae');
load bodyfat_dataset
layers = [
sequenceInputLayer(13)
lstmLayer(40)
maeRegressionLayer('mae')];
options = trainingOptions('sgdm');
net = trainNetwork(bodyfatInputs,bodyfatTargets,layers,options);
Thank you for the help!
Fabrizio Bernardi
Fabrizio Bernardi 2020-8-31
编辑：Fabrizio Bernardi 2020-8-31
Edit: now I tried with
layers = [
sequenceInputLayer(13)
%lstmLayer(40)
fullyConnectedLayer(20)
reluLayer % optional
fullyConnectedLayer(1)
maeRegressionLayer('mae')];
and it gives result! Even if with
YPred = predict(net,bodyfatInputs);
predictionError = YPred - bodyfatTargets;
The erros are very large and the output are almost all the same though...

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