Introduction

Recurrent Neural Networks (RNNs) are a cornerstone in sequence modeling tasks, particularly in areas like time-series analysis, natural language processing, and sequence prediction. Keras, a high-level neural networks API, provides an accessible platform to implement RNNs. However, preparing input data for RNNs requires careful attention to the format and structure. This article covers how to prepare input data specifically for RNNs in Keras, detailing the necessary preprocessing steps and addressing common pitfalls.

Understanding Input Shape for RNNs

When configuring the input for an `RNN` in Keras, it's crucial to understand the structure that the model expects. Generally, the input data should be a 3-dimensional array with the following dimensions:

• Samples: number of sequences in a batch.
• Timesteps: length of a sequence.
• Features: number of features per timestep.

The input shape, therefore, should be structured as `(batch_size, timesteps, features)`. When working with Keras, you may set specific `batch_size` or leave it as `None` to mean flexible batch sizes during training and inference.

Preparing Data: Steps and Techniques

1. Gathering and Splitting Data:
   • Collect raw data and split it into training, validation, and test sets. The split should maintain the sequence order.
   • Use `train_test_split` provided by libraries like Scikit-learn, if data shuffling is not an issue.
2. Feature Scaling:
   • Scale the data to be in a suitable range (e.g., between 0 and 1). Use MinMaxScaler or StandardScaler from Scikit-learn for this. RNNs are sensitive to input scales, and normalization/standardization helps in speeding up convergence.
   • Data for RNNs must be presented in a sequential manner. Use sliding window techniques to rearrange the data into sequences. Decide on the sequence length based on the task or empirical testing.
   • Once sequences are created, ensure that the data is reshaped according to Keras's expectations `(samples, timesteps, features)`.
   • Define your `RNN` model with layers like `SimpleRNN`, `LSTM`, or `GRU`. Specify the `input_shape` as `(timesteps, features)`, omitting the batch size.

• Sequence Length: The sequence length (or timesteps) is a hyperparameter that must be chosen carefully. It affects the model's ability to capture dependencies. Short sequences may not capture enough context, whereas very long sequences can be costly in terms of memory and computation.
• Data Leakage: Always ensure that test data is not part of your training or validation set. Especially for time-series, leakage can arise if future data points leak into training sequences.