Problem in automatic gradient calculation using Deep Learning Toolbox

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Hello,
Could anyone please tell me why the dlgradient function is throwing the error? It seems to me that somehow the loss is not getting traced to the input weights? Could anyone suggest why it is happening?
x0 = dlarray([1,2,3],'BC');
y0 = dlarray(14,'BC');
fcnn_graph = layerGraph;
layers = [featureInputLayer(3,"Normalization","none","Name","InputLayer")
fullyConnectedLayer(5,"WeightsInitializer","glorot","BiasInitializer","ones","Name","fc1")
tanhLayer("Name","active_1")
fullyConnectedLayer(5,"WeightsInitializer","glorot","BiasInitializer","ones","Name","fc2")
tanhLayer("Name","active_2")
fullyConnectedLayer(1,"WeightsInitializer","glorot","BiasInitializer","ones","Name","fc3")
];
fcnn_graph = addLayers(fcnn_graph,layers);
dlnet = dlnetwork(fcnn_graph);
[gradients,state,loss] = modelGradients(dlnet,x0,y0);
function [gradients,state,loss] = modelGradients(dlnet,X,Y)
[YPred,state] = forward(dlnet,X);
loss = crossentropy(YPred,Y);
gradients = dlgradient(loss,dlnet.Learnables);
loss = double(gather(extractdata(loss)));
end

Accepted Answer

Philip Brown
Philip Brown on 9 Sep 2021
To ensure tracing is happening, you need to pass your modelGradients function to dlfeval - see this doc page for more details. Replace your line:
[gradients,state,loss] = modelGradients(dlnet,x0,y0);
with
[gradients,state,loss] = dlfeval(@modelGradients, dlnet,x0,y0);
This will ensure that your modelGradients function has tracing between the input weights and the loss.
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