TensorFlow Feed List Feature (Multi-Hot Encoding) to `tf.estimator`

TensorFlow, an open-source platform for machine learning developed by the TensorFlow team at Google, has grown into one of the most widely adopted frameworks in both industry and academia. One of its most robust features is the `tf.estimator` API, which simplifies the training and deployment of machine learning models. In this article, we explore how to input feed list features encoded as multi-hot vectors into the `tf.estimator` framework.

Understanding Multi-Hot Encoding

Multi-hot encoding, similar to one-hot encoding, is a process used to transform categorical variables into a vector of binary values (0s and 1s). However, unlike one-hot encoding where only one index is set to 1, multi-hot encoding can have multiple indices with 1s, representing multiple categories associated with a single feature. This encoding is particularly useful in scenarios where items can simultaneously belong to multiple categories or classes, such as in text categorization, tagging, and recommendation systems.

Example:

Consider a movie dataset where a movie could belong to multiple genres. Suppose you have three genres: Action, Comedy, Drama. If a movie belongs to both Action and Comedy, its multi-hot encoded feature would be `[1, 1, 0]`.

Feeding Multi-Hot Encoded Features into `tf.estimator`

The `tf.estimator` API is designed for easy and efficient model training and evaluation. To feed multi-hot encoded features into an `Estimator`, follow these steps:

1. Prepare the dataset:

Assume a dataset with multi-hot encoded features for the movie genres is available as follows:

• Categorical features do not fit into mutually exclusive categories.
• The feature space is sparse, and multiple attributes describe a single data point.
• High-dimensional datasets may result from many categorical features.
• The memory footprint may increase due to dense representation.