Time series training using 2D CNN

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Hi ,

I am trying to use 2D CNN to train and then predict time series (specifically analog signal splitted into 5 samples each sequence ---> the whole input matrix is Nx5) ...

Though i defined 4d matrices XTrain and YTrain for trainNetwork() function as follows :

... COMMENTS ...

I defently defined 4d matrix with images 1xchannel_length but still getting the error below :

"

>> MatlabNnPilot

155 net = trainNetwork(XTrain,YTrain,layers,options);

Error using trainNetwork (line 165)

Invalid training data. X must be a 4-D array of images.

Error in MatlabNnPilot (line 155)

net = trainNetwork(XTrain,YTrain,layers,options);

"

Please advise how to resovle it if possible ?

Igor

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igor Lisogursky 2020-9-27

As well attaching here the sizes of XTrain and YTrain from the same code :

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Answer 1

Srivardhan Gadila 2020-9-28

在 MATLAB Online 中打开

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I tried the following code which is written based on the above mentioned code & I'm not getting any errors. You can refer to the net = trainNetwork(X,Y,layers,options) syntax and also it's corresponding Input Arguments description.

Try checking the following code once:

input_size = 5;
output_size = 1;
numHiddenUnits = 32;
epochs = 50;
nTrainSamples = 40725;
layers = [ ...
    imageInputLayer([1 input_size 1],'Name','input')
    
    convolution2dLayer([1 input_size],1,'Name','conv')
    batchNormalizationLayer('Name','bn')
    reluLayer('Name','relu')
    
    fullyConnectedLayer(output_size, 'Name','fc')
    regressionLayer('Name','regression')];
% lgraph = layerGraph(layers);
% analyzeNetwork(layers)
%%
trainData = randn([1 5 1 nTrainSamples]);
% trainLabels = randn(nTrainSamples,numClasses);
trainLabels = randn([1 1 1 nTrainSamples]);
size(trainData)
size(trainLabels)
%%
options = trainingOptions('adam', ...
    'InitialLearnRate',0.005, ...
    'ValidationData',{trainData,trainLabels},...
    'LearnRateSchedule','piecewise',...
    'MaxEpochs',epochs, ...
    'MiniBatchSize',32, ...
    'Verbose',1, ...
    'Plots','training-progress');
net = trainNetwork(trainData,trainLabels,layers,options);

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Srivardhan Gadila 2020-10-6

@igor Lisogursky, you can verify the same by creating your network and using analyzeNetwork function to view the shape of the activations after each layer.

igor Lisogursky 2020-10-9

Thanks @Srivardhan Gadila for a responde it will be usefull func

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Time series training using 2D CNN

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