Previously accessible file is now inaccessible.
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Hi everyone, I was using deep learning toolbox to train a CNN. After I edit my code (which can successfully run), it shows the following wrong message "Previously accessible file "inmem:///deep_learning/tpc723775a_5df4_4b61_96f1_552a4a17e7e2.m" is now inaccessible."
Previously accessible file "inmem:///deep_learning/tp7a8bcbea_11cf_4603_8ba8_f6278a23f4fa.m" is now inaccessible.
Error in deep.internal.recording.convert.tapeToFunction>@(varargin)fcnWithConstantsInput(varargin{:},constants) (line 37)
fcn = @(varargin)fcnWithConstantsInput(varargin{:},constants);
Error in deep.internal.AcceleratedOp/backward (line 69)
[varargout{1:op.NumGradients}] = backwardFun(varargin{:});
Error in deep.internal.recording.RecordingArray/backwardPass (line 89)
grad = backwardTape(tm,{y},{initialAdjoint},x,retainData,false,0);
Error in dlarray/dlgradient (line 132)
[grad,isTracedGrad] = backwardPass(y,xc,pvpairs{:});
Error in test_modelGradients320 (line 14)
[gradientsSubnet,gradientsParams] = dlgradient(loss,dlnet.Learnables,fcParams);
Error in deep.internal.dlfeval (line 17)
[varargout{1:nargout}] = fun(x{:});
Error in dlfeval (line 40)
[varargout{1:nargout}] = deep.internal.dlfeval(fun,varargin{:});
Error in test_siamese320 (line 126)
[gradientsSubnet,gradientsParams,loss] = dlfeval(@test_modelGradients320,dlnet,fcParams,dlX1,dlX2,pairLabels,name);
Actually, I didn't find so named file existing anywhere in my computer. I have searched and tried all potential solutions but still cannot solve this problem. The most terrible thing is that it still doesn't work even if I rewrite the code in a new script.
I was using win10+matlab 2021b. Hope for any helpful solutions, thank you!
8 Comments
Richard
on 24 Mar 2022
@Cloud Wind thanks for the code, I think this will be very helpful for us in understanding the exact nature of the issue.
For now, the best suggestion I have is to continue using Acceleration="none".
Accepted Answer
Richard
on 24 Mar 2022
The errot itself is related to performance optimizations within the dlnetwork class. You should be able to prevent it by specifying ("Acceleration", "none") as an additional parameter-value pair when you call forward on the network, i,.e.:
Y = forward(net, X, "Acceleration", "none")
(Obviously this may also have an adverse impact on performance, unfortunately)
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