论文标题
Tensorx：可扩展的神经网络模型设计和部署的API
TensorX: Extensible API for Neural Network Model Design and Deployment
论文作者
论文摘要
Tensorx是一个用于张曲流中复杂神经网络模型的原型，设计和部署的Python库。特别强调易用性，性能和API一致性。它的目的是使实际上易于组合和重复使用的神经网络层（如神经网络层）提供可用的高级组件。它的体系结构允许在研究或工业环境建立神经网络模型时表达通常发现的模式。结合了其他几个深度学习库的想法，它使使用最先进模型中常见的组件变得易于使用。库设计将功能数据流计算图与面向对象的神经网络构建块混合在一起。 Tensorx结合了Python的动态性质与张力量的高性能GPU操作。 该库具有最小的核心依赖性（Tensorflow和Numpy），并根据Apache Licens 2.0许可分发，鼓励其在学术和商业环境中使用。可以在https://tensorx.org/中找到完整的文档，源代码和二进制文件。
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/.
