# How to do the sum for 2 gradient objects in the deep learning toolbox?

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SC on 27 Nov 2019
Answered: Sourav Bairagya on 10 Dec 2019
Hi,
I have gradients1 and gradients2 which have exactly same structure but different numerical values. How can I do the sum? Current I tried gradients1+gradients2 but I got error.
Thanks!
My code:
rng(123); % seed
X_ori=[4,163,80;5,164,75]; % data; #(number) = 2; #(features) = 3;
X=permute(X_ori,[3,4,2,1]);
dlX = dlarray(X, 'SSCB');
Y_ori=[0, 0, 0, 1; 0, 1, 0, 0]; % data labels (i.e. one-hot vectors for 4 classes)
myModel = [
imageInputLayer([1 1 3],'Normalization','none','Name','in')
fullyConnectedLayer(7,'Name','Layer 1')
fullyConnectedLayer(4,'Name','Layer 2')];
MyLGraph = layerGraph(myModel);
myDLnet = dlnetwork(MyLGraph);
CorrectLabels_transpose=transpose(CorrectLabels);
[modelOutput,state] = forward(myModel,modelInput);
loss = -31*sum(sum(CorrectLabels_transpose.*log(sigmoid(modelOutput/100))));
end
CorrectLabels_transpose=transpose(CorrectLabels);
[modelOutput,state] = forward(myModel,modelInput);
loss = -42*sum(sum(CorrectLabels_transpose.*log(sigmoid(modelOutput/100))));
end
SC on 30 Nov 2019
Edited: SC on 30 Nov 2019
I can change the line "gradients_sum = gradients1+gradients2;" to the followings and then I can get the sum. But I still want to know if there are some more efficient ways to do so.
for i=1:num_layers
end
end

Sourav Bairagya on 10 Dec 2019
As in this case, 'gradients1.Value' and 'gradients2.Value' both are cell arrays and each one contains another cell arrays as elements within it, hence, direct conversion of these two cell arrays into matrices using 'cell2mat' or direct addition of them using '+' operator is not possible. Hence, you have to access each element individually and add them.

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