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I've been learning about LSTMs and I commonly see them applied to the same type of task. For example, given a 1D list of values, predict which class they belong to. Or, given 8 values from 8 sensors, predict a state.

What I am trying to figure out, is if I have samples with shape (20, N), can I give each 20xN sample a binary classification using only an LSTM? I was hoping to treat each 20x1 column of the row as a timestep. Unfortunately I have only seen examples where each 20x1 column can be treated as a sequence. Is it possible for each timestep in an LSTM to have multiple input variables?

Thanks in advance.

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Update: I have learned that this is possible. In keras, the parameters for the LSTM layer go in this order: batch, sequence_length, num_features

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