How to use multiple data in LSTM?
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daemo Lee
il 12 Gen 2022
Commentato: Pratyush Roy
il 19 Gen 2022
In this example https://www.mathworks.com/help/deeplearning/ug/time-series-forecasting-using-deep-learning.html,
- Discription said that is 'For single time step predictions, use the CPU.' I wonder how to do multi time step pridiction in Matlab.
- Espacially, I would like to know about the way to use multi training data set for LSTM, not single training data set like this example.
That example used a double data(1xN), but I hope to enter multiple(M) double data(like MxN).
Please let me know some idea or give your knowledge.
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Pratyush Roy
il 17 Gen 2022
Hi Daerno.
The example mentioned in the question is used for finding temporal relation between 1-D input and 1 dimensional output. As mentioned in the code:
numFeatures = 1;
numResponses = 1;
numHiddenUnits = 200;
layers = [ ...
sequenceInputLayer(numFeatures)
lstmLayer(numHiddenUnits)
fullyConnectedLayer(numResponses)
regressionLayer];
The number of features is 1. Hence the data passed as input is 1-dimensional in nature.
In general, LSTMs are built to work for multi-dimensional data. We can change the numFeatures and numResponses value to map one single/multi-dimensional vector to another single/multi-dimensional vector. This doc link captures a example involving multi-dimensional vectors.
Hope this helps!
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Pratyush Roy
il 19 Gen 2022
Hi Daemo,
Since you have multiple datasets, you can train multiple LSTMs in parallel. Please refer to the doc link below for more details:
Hope this helps!
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