i have a problem with modelGradients

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When i run thus section :
for numEpoch = 1:nEpochs
reset(preprocessedTrainingData);% Reset datastore.
iteration = 0;
while hasdata(preprocessedTrainingData)
t_start = tic;
% Custom training loop.
% Read batch of data and create batch of images and
% ground truths.
outDataTable = read(preprocessedTrainingData);
XTrain = outDataTable{1,1}{1};
YTrain = outDataTable{1,2}{1};
if isempty(YTrain)
% Convert mini-batch of data to dlarray.
XTrain = dlarray(single(XTrain),'SSCB');
% Evaluate the model gradients and loss using dlfeval and the
% modelGradients function.
[gradients,boxLoss,objLoss,clsLoss,totalLoss,state] = dlfeval(@modelGradients, model, XTrain, YTrain,yoloLayerNumber);
I get this error :
'modelGradients' is used in Generate Synthetic Signals Using Conditional Generative Adversarial Network.
Error in deep.internal.dlfeval (line 18)
[varargout{1:nout}] = fun(x{:});
Error in dlfeval (line 41)
[varargout{1:nout}] = deep.internal.dlfeval(fun,varargin{:});
Error in nouveux (line 94)
[gradients,boxLoss,objLoss,clsLoss,totalLoss,state] = dlfeval(@modelGradients, model, XTrain,

Accepted Answer

Arianna Pryor
Arianna Pryor on 12 May 2021
I got a similar error when trying to run the VAE example. I had to use another modelGradients function.
function [infGrad, genGrad] = modelGradients(encoderNet, decoderNet, x)
[z, zMean, zLogvar] = sampling(encoderNet, x);
xPred = sigmoid(forward(decoderNet, z));
loss = ELBOloss(x, xPred, zMean, zLogvar);
[genGrad, infGrad] = dlgradient(loss, decoderNet.Learnables, ...

More Answers (1)

Mohamed Marei
Mohamed Marei on 26 Jul 2021
Edited: Mohamed Marei on 26 Jul 2021
Because MATLAB sees both of these functions which share the same name on the path, it doesn't know which one to use for each example. Therefore, it may be better practice to copy the internals of this function into a different function (more appropriate for your example), i.e.
function [gradients, boxLoss, objLoss, clsLoss, totalLoss, state] = ...
yoloModelGradients(network, Xtrain, Ytrain, yoloLayerNumber)
% loss, gradients, and states definitions go here.
After that, replace the call to the old modelGradients function in your call to dlfeval:
% Evaluate the model gradients and loss using dlfeval and the
% yoloModelGradients function.
[gradients,boxLoss,objLoss,clsLoss,totalLoss,state] = dlfeval(@yoloModelGradients, model, XTrain, YTrain,yoloLayerNumber);
Hope you've managed to get it fixed since then!

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