AI Product Management

How to Reshape Data for Recurrent Neural Networks

Insights on understanding data shape, padding sequences, and batching for effective RNN training.

AI Product Managers must understand how to reshape data effectively for Recurrent Neural Networks (RNNs). This guide covers key takeaways for preparing your data: understanding data shape, padding sequences, and batching for efficient training. These steps ensure your RNN models perform optimally and handle various data challenges.

1. Understand Your Data Shape

To effectively use RNNs, recognize the format of your input data. Typically, RNNs process sequences. Your data should have three dimensions:

• the number of sequences,
• sequence length,
• and the number of features.

For instance, a dataset of 1000 sequences, each with 10 time steps and 3 features per step, should have a shape of (1000, 10, 3).

2. Determine the Shape of Your Data

RNNs require input data in the shape of (number of sequences, sequence length, number of features). Clarify these dimensions in your dataset to prepare it correctly for…