How Do Time Steps and Features Affect Keras LSTM Performance?

Understanding Time Steps and Features in Keras LSTMs

To answer your questions, let's dig deeper into the concepts of time steps and features in relation to LSTMs implemented in Keras.

Time Steps and Features

In the Keras example provided, the trainX data is reshaped into the following shape:

(trainX.shape[0], look_back, 1)

• Time steps: The look_back value represents the number of time steps in each sequence. Here, it's set to 3, meaning each row in trainX contains the three previous data points.
• Features: The number of features in a sequence corresponds to the last dimension of the data tensor. In this case, it's 1, indicating the sequences contain only one feature (e.g., a stock price).

Regarding the image you linked from Karpathy's blog, each "pink" rectangle represents one feature, and the "green" rectangles indicate time steps. Thus, the diagram in the image would correspond to a time series with three time steps and two features.

Stateful LSTMs

Stateful LSTMs maintain an internal state that allows them to remember what they have learned during the processing of a given sequence. When using them with Keras, you set the stateful parameter to True.

In your example, you set batch_size to 1 and use model.fit() for training with shuffle=False. This means that each batch contains a single sequence, and the LSTM is processing the sequences in the same order they appear in the training data. As a result, the state of the LSTM is preserved across batches, allowing it to learn from the entire sequence.

By resetting the LSTM's state between training epochs, you effectively "start over" the learning process for each epoch. However, the LSTM still remembers the overall patterns it has learned across epochs.

Important Notes

• If you have multiple independent sequences (e.g., modeling two stock prices simultaneously), each sequence should have its own LSTM layer.
• The number of time steps must be determined based on the specific problem and data you are working with.
• Stateful LSTMs can be useful when you want to learn dependencies over long sequences, but they should be used with caution as they can introduce overfitting.

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