Time series training using 2D CNN
3 views (last 30 days)
Show older comments
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
Answers (1)
Srivardhan Gadila
on 28 Sep 2020
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);
5 Comments
Srivardhan Gadila
on 6 Oct 2020
@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.
See Also
Categories
Find more on Image Data Workflows in Help Center and File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!