Creating and using Datastore for LSTM time sequence data
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I have time sequence data files more than 10000 numbers stored individually at csv files. Each sequence data file consists of a sample of data from 6300 features taken at 5 time sequences. Each column is a measurement data from a feature. The labels are stored in separate file sequencially.
-0.7 -1.7 -5.09 -4.79 ....
-0.7 -1.7 -5.09 -4.79 ....
-1.06 -1.59 -5.08 -4.76 .....
-1.42 -1.86 -5.61 -4.86 ....
-1.34 -2.01 -5.1 -4.62 .....
numFeatures= 6300;
numHiddenUnits = 100;
numClasses = 3;
layers = [ ...
sequenceInputLayer(numFeatures)
lstmLayer(numHiddenUnits,'OutputMode','last')
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer];
options = trainingOptions('adam', ...
'MiniBatchSize',20,...
'MaxEpochs',10, ...
'Shuffle','once',...
'GradientThreshold',0.001, ...
'Verbose',1, ...
'Plots','training-progress');
I want to use the data for LSTM classification. I could not load all the data for training purpose.
Matlab asks for cell data for each time sequence sample data for training.
So, How can I load the files and train the network using the datastore for such large data?
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Angelo Yeo
2024-2-11
0 个投票
tabularTextDatastore supports to manage a large set of "csv" files. To quote from the doc:
Use a TabularTextDatastore object to manage large collections of text files containing column-oriented or tabular data where the collection does not necessarily fit in memory.
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