How to solve Unable to read file: 'E:\TIME-I​Q\128QAM\f​rame_snr30​iq_128QAM_​100.mat'?

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john karli
john karli 2021-11-8
I have dataset in .mat file and shape of dataset is [2,1280,2]. I have change the inpu layer of the model by using the deepNetworkDesigner app of matlab. All code works good but when i run below command i got the following error.
net = trainNetwork(imdsTrain,lgraph,options);
error
Error using trainNetwork (line 184)
Unable to read file: 'E:\TIME-IQ\128QAM\frame_snr30iq_128QAM_100.mat'.
Caused by:
Error using matlab.io.datastore.ImageDatastore/read (line 77)
Unable to read file: 'E:\TIME-IQ\128QAM\frame_snr30iq_128QAM_100.mat'.
Error using matlab.io.datastore.exceptions.decorateCustomFunctionError>generateReadFcnError (line 103)
Error using ReadFcn @readDatastoreImage for file:
E:\TIME-IQ\128QAM\frame_snr30iq_128QAM_100.mat
Error using imread>get_format_info (line 545)
Unable to determine the file format.
Error in imread (line 391)
fmt_s = get_format_info(fullname);
Error in readDatastoreImage (line 12)
data = imread(filename);
full code
imds = imageDatastore('E:\TIME-IQ\', ...
'IncludeSubfolders',true, 'FileExtensions','.mat',...
'LabelSource','foldernames');
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');%%
net = lgraph_2;
net(1)
inputSize = lgraph_2.Layers(1).InputSize;
[learnableLayer,classLayer] = findLayersToReplace(lgraph_2);
[learnableLayer,classLayer]
numClasses = numel(categories(imdsTrain.Labels));
if isa(learnableLayer,'nnet.cnn.layer.FullyConnectedLayer')
newLearnableLayer = fullyConnectedLayer(numClasses, ...
'Name','new_fc', ...
'WeightLearnRateFactor',10, ...
'BiasLearnRateFactor',10);
elseif isa(learnableLayer,'nnet.cnn.layer.Convolution2DLayer')
newLearnableLayer = convolution2dLayer(1,numClasses, ...
'Name','new_conv', ...
'WeightLearnRateFactor',10, ...
'BiasLearnRateFactor',10);
end
lgraph_2 = replaceLayer(lgraph_2,learnableLayer.Name,newLearnableLayer);
newClassLayer = classificationLayer('Name','new_classoutput');
lgraph = replaceLayer(lgraph_2,classLayer.Name,newClassLayer);
miniBatchSize = 128;
valFrequency = floor(numel(imdsTrain.Files)/miniBatchSize);
checkpointPath = pwd;
options = trainingOptions('sgdm', ...
'MiniBatchSize',miniBatchSize, ...
'MaxEpochs',100, ...
'InitialLearnRate',1e-3, ...
'Shuffle','every-epoch', ...
'ValidationData',imdsValidation, ...
'ValidationFrequency',valFrequency, ...
'Verbose',false, ...
'Plots','training-progress', ...
'CheckpointPath',checkpointPath);
net = trainNetwork(imdsTrain,lgraph,options);
Please help

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