TensorX: Extensible API for Neural Network Model Design and Deployment

TL;DR

TensorX is a Python library that simplifies the design, prototyping, and deployment of complex neural networks in TensorFlow, emphasizing ease of use, performance, and API consistency.

Contribution

It introduces a high-level, extensible API combining functional and object-oriented approaches for neural network construction in TensorFlow.

Findings

Supports complex neural network patterns

Enhances reusability of neural network components

Achieves high performance with GPU acceleration

Abstract

TensorX is a Python library for prototyping, design, and deployment of complex neural network models in TensorFlow. A special emphasis is put on ease of use, performance, and API consistency. It aims to make available high-level components like neural network layers that are, in effect, stateful functions, easy to compose and reuse. Its architecture allows for the expression of patterns commonly found when building neural network models either on research or industrial settings. Incorporating ideas from several other deep learning libraries, it makes it easy to use components commonly found in state-of-the-art models. The library design mixes functional dataflow computation graphs with object-oriented neural network building blocks. TensorX combines the dynamic nature of Python with the high-performance GPU-enabled operations of TensorFlow. This library has minimal core dependencies (TensorFlow and NumPy) and is distributed under Apache License 2.0 licence, encouraging its use in both an academic and commercial settings. Full documentation, source code, and binaries can be found in https://tensorx.org/.