TensorFlow Sparse Tensors Explained With Examples

Sparse tensors efficiently store and process data with many zero values. TensorFlow offers robust support for them using tf.sparse.SparseTensor, with utilities to convert, manipulate, and integrate sparse data across models, datasets, and preprocessing workflows in NLP, computer vision, and more.

Content Overview

• Sparse tensors in TensorFlow
• Creating a tf.sparse.SparseTensor
• Manipulating sparse tensors
• Using tf.sparse.SparseTensor with other TensorFlow APIs
• tf.keras
• tf.data
• tf.train.Example
• tf.function
• Further reading and resources

When working with tensors that contain a lot of zero values, it is important to store them in a space- and time-efficient manner. Sparse tensors enable efficient storage and processing of tensors that contain a lot of zero values. Sparse tensors are used extensively in encoding schemes like TF-IDF as part of data pre-processing in NLP applications and for pre-processing images with a lot of ...