What are Ragged Tensors?

In TensorFlow, tensors are the basic building blocks for data representation. A tensor is essentially a multi-dimensional array, where each dimension represents a different mode of indexing. Ragged tensors, however, deviate from this notion by allowing for variable lengths along certain dimensions.

Ragged tensors possess several distinct features:

1. Rank: Similar to conventional tensors, ragged tensors have a rank, denoting the number of axes they contain.
2. Shape: Unlike regular tensors, the shape of a ragged tensor isn’t a fixed tuple of dimensions. Instead, it comprises a nested structure indicating the lengths of each dimension for every element, allowing for variations in shape.
3. Values: Ragged tensors can contain scalars, vectors, matrices, or even other ragged tensors, providing additional versatility in data representation.

Ragged tensors are a fundamental data structure in TensorFlow, especially in scenarios where data doesn’t conform to fixed shapes, such as sequences of varying lengths or nested structures. In this article, we’ll understand what ragged tensors are, why they’re useful, and provide hands-on coding examples to illustrate their usage.