In recent years, the importance of deep learning has significantly increased in pattern recognition, computer vision, and artificial intelligence research, as well as in industry. However, despite the existence of multiple deep learning frameworks, there is a lack of comprehensible and easy-to-use high-level tools for the design, training, and testing of deep neural networks (DNNs). In this paper, we introduce Barista, an open-source graphical high-level interface for the Caffe deep learning framework. While Caffe is one of the most popular frameworks for training DNNs, editing prototext files in order to specify the net architecture and hyper parameters can become a cumbersome and error-prone task. Instead, Barista offers a fully graphical user interface with a graph-based net topology editor and provides an end-to-end training facility for DNNs, which allows researchers to focus on solving their problems without having to write code, edit text files, or manually parse logged data.
翻译:近年来,深层学习的重要性在模式识别、计算机视觉和人工智能研究以及工业领域都大大增加了,然而,尽管存在多个深层学习框架,但缺乏设计、培训和测试深层神经网络的易懂和易于使用的高级工具。本文介绍Barista,这是Cafe深层学习框架的开放源图形高层次界面。虽然Caffe是培训DNS的最受欢迎的框架之一,但编辑原始文本文件以具体确定网络架构和超常参数可能成为一种繁琐和易出错的任务。相反,Barista为基于图表的净表层编辑提供了一个完全图形化的用户界面,并为DNNS提供了一个端对端的培训设施,使研究人员能够集中精力解决他们的问题,而不必写代码、编辑文本文件或人工粗略的登录数据。