Time series training using 2D CNN
3 ビュー (過去 30 日間)
古いコメントを表示
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
回答 (1 件)
Srivardhan Gadila
2020 年 9 月 28 日
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 件のコメント
Srivardhan Gadila
2020 年 10 月 6 日
@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.
参考
カテゴリ
Help Center および File Exchange で Image Data Workflows についてさらに検索
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!