论文标题
原型型：神经网络集成设计和开发环境
PrototypeML: A Neural Network Integrated Design and Development Environment
论文作者
论文摘要
神经网络体系结构最常在概念上设计和描述，但通过编写容易出错的代码来实现。 PrototyPeml是一个机器学习开发环境，它桥接了设计和开发过程之间的二分法：它提供了高度直观的视觉神经网络设计界面，支持（但摘要）Pytorch深度学习框架的完整能力，减少模型设计和开发时间，使模型设计和开发变得更加轻松，并使许多框架和编码互动iDiosyncrassies自动化。在本文中，我们详细介绍了推动原型实施的深度学习发展缺陷，并提出了一种解决这些问题的混合方法，而无需限制网络表现力或降低代码质量。我们证明了视觉方法对研究，行业和教学的神经网络设计的现实益处。可在https://prototypeml.com上找到
Neural network architectures are most often conceptually designed and described in visual terms, but are implemented by writing error-prone code. PrototypeML is a machine learning development environment that bridges the dichotomy between the design and development processes: it provides a highly intuitive visual neural network design interface that supports (yet abstracts) the full capabilities of the PyTorch deep learning framework, reduces model design and development time, makes debugging easier, and automates many framework and code writing idiosyncrasies. In this paper, we detail the deep learning development deficiencies that drove the implementation of PrototypeML, and propose a hybrid approach to resolve these issues without limiting network expressiveness or reducing code quality. We demonstrate the real-world benefits of a visual approach to neural network design for research, industry and teaching. Available at https://PrototypeML.com
