Keras LSTM: What are Timesteps and Features, and How Does Stateful LSTM Leverage Sequential Information?

Understanding Keras LSTM

What are time steps and features?

The time step and features are specified by the last two dimensions of the tensor.

• Time Step: Enter the number of steps in the sequence.
• Feature: The number of values ​​at each time step in the input sequence.

According to the code provided in the question, trainX is a 3D array with time steps of 3 and features of 1. This shows that the model is considering a many-to-one situation, where the 3 pink boxes correspond to multiple inputs.

Stateful LSTM

Stateful LSTM allows the model to retain cell state values ​​across batches. When batch_size is 1, memory is reset between training runs. This helps the model remember previous steps in the sequence for more accurate predictions. In this example, batch_size is set to 1 and the data is not shuffled, meaning the model will see the data sequentially and take advantage of the sequence information.

Example diagram

The image you provided corresponds to the following Keras model:

Figure 1:

• Keras will process the input sequence in a many-to-many manner.
• return_sequences=True allows the layer to output sequences at each time step.

Figure 2:

• stateful=True allows the model to retain state across batches.
• The red box in each row represents a batch of the original sequence.
• The green box in each row represents the sequence output by the model in each batch.
• Contiguous rows indicate that the model treats the entire sequence as one continuous sequence, even if it is fed into the model in batches.

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