TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning
This provides an accessible tool for non-specialists and researchers to perform distributed machine learning, though it is incremental as it builds on existing TensorFlow infrastructure.
The authors introduced TF.Learn, a high-level Python module for distributed machine learning within TensorFlow, which simplifies model creation and training using a Scikit-learn style interface, enabling non-specialists and researchers to efficiently handle small to large-scale problems.
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.