Error in matlab included deep learning example

Question

Javier Bush 2019-10-15

1
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/485341-error-in-matlab-included-deep-learning-example

编辑： Walter Roberson 2019-12-30

采纳的回答： Joss Knight

在 MATLAB Online 中打开

I am trying to run the matlab example

openExample('nnet/SeqToSeqClassificationUsing1DConvAndModelFunctionExample')

In 2019b but, when i change to train the network on gpu the example show me this error. Please help me to run it or give me a workaround to train using gpu.

Error using gpuArray/subsasgn
Attempt to grow array along ambiguous dimension.
Error in deep.internal.recording.operations.ParenAssignOp/forward (line 45)
            x(op.Index{:}) = rhs;
Error in deep.internal.recording.RecordingArray/parenAssign (line 29)
    x = recordBinary(x,rhs,op);
Error in dlarray/parenAssign (line 39)
    objdata(varargin{:}) = rhsdata;
Error in SeqToSeqClassificationUsing1DConvAndModelFunctionExample>maskedCrossEntropyLoss (line 484)
    loss(i) = crossentropy(dlY(:,i,idx),dlT(:,i,idx),'DataFormat','CBT');
Error in SeqToSeqClassificationUsing1DConvAndModelFunctionExample>modelGradients (line 469)
loss = maskedCrossEntropyLoss(dlY, dlT, numTimeSteps);
Error in deep.internal.dlfeval (line 18)
[varargout{1:nout}] = fun(x{:});
Error in dlfeval (line 40)
[varargout{1:nout}] = deep.internal.dlfeval(fun,varargin{:});
Error in SeqToSeqClassificationUsing1DConvAndModelFunctionExample (line 284)
        [gradients, loss] = dlfeval(@modelGradients,dlX,Y,parameters,hyperparameters,numTimeSteps);
 

Thanks!

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Edric Ellis 2019-10-15

Thanks for reporting this - I can reproduce the problem using R2019b here, I shall forward this to the development team...

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Answer 1

Joss Knight 2019-10-15

2
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https://ww2.mathworks.cn/matlabcentral/answers/485341-error-in-matlab-included-deep-learning-example#answer_396426

在 MATLAB Online 中打开

There is a bug in this Example which will be rectified. Thanks for reporting. To workaround, initialize the loss variable in the maskedCrossEntropyLoss function:

function loss = maskedCrossEntropyLoss(dlY, dlT, numTimeSteps)
numObservations = size(dlY,2);
loss = zeros([1,1],'like',dlY); % Add this line
for i = 1:numObservations
    idx = 1:numTimeSteps(i);
    loss(i) = crossentropy(dlY(:,i,idx),dlT(:,i,idx),'DataFormat','CBT');
end
end

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Katja Mogalle 2019-10-25

There are some small issues in the example script that will prevent you from setting the miniBatchSize>1. The fix is pretty simple though.

1) Replace the modelGradients function with the following:

function [gradients,loss] = modelGradients(dlX,T,parameters,hyperparameters,numTimeSteps)
dlY = model(dlX,parameters,hyperparameters,true);
dlY = softmax(dlY,'DataFormat','CBT');
dlT = dlarray(T,'CBT');
loss = maskedCrossEntropyLoss(dlY, dlT, numTimeSteps);
gradients = dlgradient(mean(loss),parameters); % this line was changed to compute the mean loss
end

2) Replace the transformSequences function with the following:

function [XTransformed, YTransformed, numTimeSteps] = transformSequences(X,Y)
% Removed line which computed the numTimeSteps. We'll compute this later in the loop
miniBatchSize = numel(X);
numFeatures = size(X{1},1);
sequenceLength = max(cellfun(@(sequence) size(sequence,2),X));
classes = categories(Y{1});
numClasses = numel(classes);
sz = [numFeatures miniBatchSize sequenceLength];
XTransformed = zeros(sz,'single');
sz = [numClasses miniBatchSize sequenceLength];
YTransformed = zeros(sz,'single');
for i = 1:miniBatchSize
    predictors = X{i};
    
    % Create dummy labels.
    numTimeSteps(i) = size(predictors,2); % This line now sets the time steps for the i-th observation
    responses = zeros(numClasses, numTimeSteps(i), 'single'); % This line also uses the i-th observation numTimeSteps
    for c = 1:numClasses
        responses(c,Y{i}==classes(c)) = 1;
    end
    
    % Left pad.
    XTransformed(:,i,:) = leftPad(predictors,sequenceLength);
    YTransformed(:,i,:) = leftPad(responses,sequenceLength);
end
end

Note, however, that depending on your GPU you might run into out-of-memory issues already with a small miniBatchSize. I have a GeForce GTX 1080 and I already run into this issue with a miniBatchSize of 3.

We will work on updating the example to fix these issues as soon as possible. Apologies for the inconvenience!

Javier Bush 2019-10-26

Thanks, I can change miniBatchSize now.

Zekun 2019-12-29

编辑：Walter Roberson 2019-12-30

在 MATLAB Online 中打开

I found another solution for

"Error using gpuArray/subsasgn

Attempt to grow array along ambiguous dimension."

In dlarray/parenAssign.m, at this location:"\R2019b\toolbox\nnet\deep\@dlarray\parenAssign.m"

Line 15:

obj = zeros(0, 0, 'like', rhs);

Replace line 15 with the following 2 lines:

    szrhs = size(rhs);
    obj = zeros(szrhs(1), szrhs(2), 'like', rhs);

Users cannot directly edit this file, so I backed it up and replace it with a new file.

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Answer 2

Javier Bush 2019-10-16

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/485341-error-in-matlab-included-deep-learning-example#answer_396544

Thanks it worked!

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Answer 3

Linda Koletsou Soulti 2019-10-22

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链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/485341-error-in-matlab-included-deep-learning-example#answer_397671

Thank you for reporting the issue. The error you are getting is related to an attempt to grow a gpuArray using linear indexing assignment.

For more information please refer to the following bug report:

https://www.mathworks.com/support/bugreports/2039937

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Javier Bush 2019-10-23

在 MATLAB Online 中打开

Linda,

I just changed the miniBatchSize to 2, in the same example and I get the following error, could you please help me with that? I think this is a bug because that is offered as a parameter in the example but you cannot change it.

Index exceeds the number of array elements (1).
Error in SeqToSeqClassificationUsing1DConvAndModelFunctionExample>maskedCrossEntropyLoss (line 486)
    idx = 1:numTimeSteps(i);
Error in SeqToSeqClassificationUsing1DConvAndModelFunctionExample>modelGradients (line 472)
loss = maskedCrossEntropyLoss(dlY, dlT, numTimeSteps);
Error in deep.internal.dlfeval (line 18)
[varargout{1:nout}] = fun(x{:});
Error in dlfeval (line 40)
[varargout{1:nout}] = deep.internal.dlfeval(fun,varargin{:});
Error in SeqToSeqClassificationUsing1DConvAndModelFunctionExample (line 287)
        [gradients, loss] = dlfeval(@modelGradients,dlX,Y,parameters,hyperparameters,numTimeSteps);

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Error in matlab included deep learning example

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