TensorBoard: Embedding Visualization

Embeddings are ubiquitous in machine learning, appearing in recommender systems, NLP, and many other applications. Indeed, in the context of TensorFlow, it's natural to view tensors (or slices of tensors) as points in space, so almost any TensorFlow system will naturally give rise to various embeddings.

TensorBoard has a built-in visualizer, called the Embedding Projector, for interactive visualization and analysis of high-dimensional data like embeddings. The embedding projector will read the embeddings from your model checkpoint file. Although it's most useful for embeddings, it will load any 2D tensor, including your training weights.

To learn more about embeddings and how to train them, see the Vector Representations of Words tutorial. If you are interested in embeddings of images, check out 登录查看完整内容