Using LSTM with a featureinputplayer

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Haoming
Haoming 2024-10-31，10:20
评论： Haoming 2024-11-11，9:20
While reading the lstm and its relative docs, i noticed that a LSTMLayer always connected after a SequenceInputLayer.
When i using lstm with a featureinputlayer and my input data is a 2-D matrix ( shape = (data_length, num_features)), I found that the network can still run. So my question is : will matlab treats it as ONE sequence with ${data_length} tokens. Or $[{data_length} sequences (sequence_length = 1)?
Thanks in advanced for your help!

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Gayathri
Gayathri 2024-11-4，6:20
Hi @Haoming,
When you use an LSTM layer in MATLAB with a “featureInputLayer” and your input data is a 2-D matrix with shape (data_length, num_features), MATLAB treats the input as one sequence with data_length tokens. For more understanding on which layer to use with LSTMs consider the below points.
  • · LSTMs are used to handle long term dependencies. So, LSTM would be used when you have data with some sort of “time ordering”.
  • · “sequenceInputLayer” is typically used when your input data is inherently sequential, such as time series data or sentences in NLP tasks. It expects data in a 3-D format: (sequence_length, num_features, num_sequences).
  • · For time series data, you can't swap the order of timesteps - this changes the meaning of your data. This is the reason why we use “sequenceInputLayer” before “lstmLayer”.
“featureInputLayer” is generally used for non-sequential data. However, when used with an LSTM, MATLAB can treat the data as a single sequence by default.
For more information on the “sequenceInputLayer” and “featureInputLayer” please refer to the below mentioned links.
https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.featureinputlayer.html
https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.sequenceinputlayer.html
Hope you find this information helpful.

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