MATLAB: Custom deep learning network – gradient function using dlfeval

custom networkdeep learningdlarraydlfevaldlgradientdlnetformatted dlarray objectsinput data must be formatted dlarray objectsmultiple inputs

I want to create a custom deep learning training function, the output of which is an array Y. I have two inputs, the arrays X1 and X2. I want to find the gradient of Y with respect to X1 and X2.
This is my network:
layers1 = [
    sequenceInputLayer(sizeInput,"Name","XTrain1")
    fullyConnectedLayer(numHiddenDimension,"Name","fc_1")
    softplusLayer('Name','s_1')];
layers2 = [
    sequenceInputLayer(sizeInput,"Name","XTrain2")
    fullyConnectedLayer(numHiddenDimension,"Name","fc_2")
    softplusLayer('Name','s_2')];
lgraph = layerGraph(layers1); 
lgraph = addLayers(lgraph,layers2); % connect layers -> 2 in, 1 out
add = additionLayer(2,'Name','add');
lgraph = addLayers(lgraph,add); 
lgraph = connectLayers(lgraph,'s_1','add/in1');
lgraph = connectLayers(lgraph,'s_2','add/in2');
fc = fullyConnectedLayer(sizeInput,"Name","fc_3");
lgraph = addLayers(lgraph,fc);
lgraph = connectLayers(lgraph,'add','fc_3');
dlnet = dlnetwork(lgraph);
My should become my output. Then every iteration, I do:
dlX1 = dlarray(X1,'CTB'); 
dlX2 = dlarray(X2,'CTB');% to differentiate: dlarray/dlgradient
for i = 1:sizeInput
    [gradx1(i), gradx2(i), dlY] = dlfeval(@modelGradientsX,dlnet,dlX1(i),dlX2(i)); % here is where I get my error
end
and I call my function , which is supposed to get the derivative of my output with respect to my inputs.
function [gradx1, gradx2, dlY] = modelGradientsX(dlnet,dlX1,dlX2)
    dlY = forward(dlnet,dlX1,dlX2); 
    [gradx1, gradx2] = dlgradient(dlY,dlX1,dlX2);
end
And the error I get is: "Input data must be formatted dlarray objects". I have seen similar approaches in other examples (like this one: https://www.mathworks.com/matlabcentral/fileexchange/74760-image-classification-using-cnn-with-multi-input-cnn) so I don't understand – why is not the correct type of data?

Best Answer

Hi,
From the code I only see a syntax error on the following line 
[gradx1(i), gradx2(i)] = dlfeval(@modelGradientsX,dlnet,dlX1(i),dlX2(i));
Here the modelGradientsX outputs three variables but you have assigned only gradx1 and gradx2 while calling it. This may be one issue. Other than that, I think loss should also be returned from the modelGradientsX function so that for next iteration the weights can be updated.
If still the error persist you may check that dlX1(i) and dlX2(i) are indeed a dlarray object because dlgradient only accept dlarray object.
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