TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning

TL;DR

TF.Learn is a high-level Python module that simplifies distributed machine learning with TensorFlow, offering an easy interface for creating, training, and evaluating models suitable for both beginners and researchers.

Contribution

It introduces a user-friendly, high-level API for TensorFlow that integrates various machine learning algorithms, facilitating easier development and benchmarking of models.

Findings

Provides an easy-to-use interface similar to Scikit-learn

Supports a wide range of algorithms for various scales

Focuses on performance, usability, and API consistency

Abstract

TF.Learn is a high-level Python module for distributed machine learning inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to simplify the process of creating, configuring, training, evaluating, and experimenting a machine learning model. TF.Learn integrates a wide range of state-of-art machine learning algorithms built on top of TensorFlow's low level APIs for small to large-scale supervised and unsupervised problems. This module focuses on bringing machine learning to non-specialists using a general-purpose high-level language as well as researchers who want to implement, benchmark, and compare their new methods in a structured environment. Emphasis is put on ease of use, performance, documentation, and API consistency.