RosenPy：用于复值神经网络的开源 Python 框架

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2024-10-18 DOI:10.1016/j.softx.2024.101925

Ariadne A. Cruz, Kayol S. Mayer, Dalton S. Arantes

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引用次数: 0

摘要

深度学习是一种重要的人工智能工具，广泛应用于工程、物理、数据科学、生物、医疗保健、农业综合企业、金融和其他许多领域。目前用于深度学习的 Python 框架，如 TensorFlow、Keras、PyTorch 和 scikit-learn 等，只能解决实际领域的问题，代表了现实世界应用的相当一部分，但不是全部。例如，复值信号对于当前和未来的电信技术至关重要。迄今为止，许多采用实值神经网络进行复域处理的研究，最终都产生了次优结果。为了满足这一需求，本文介绍了用于复值神经网络的 Python 开源框架 RosenPy。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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RosenPy: An open source Python framework for complex-valued neural networks

Deep learning is an essential artificial intelligence tool broadly used in engineering, physics, data science, biology, healthcare, agribusiness, finance, and many other areas. Current Python frameworks for deep learning, such as TensorFlow, Keras, PyTorch, and scikit-learn, only solve real-domain problems, representing a considerable part of real-world applications but not all. For instance, complex-valued signals are essential for current and future technologies in telecommunications. Thus far, numerous works employing real-valued neural networks adapted to complex-domain processing, end up generating sub-optimal results. To fulfill this demand, this article presents RosenPy, an open-source framework in Python for complex-valued neural networks.

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来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

期刊介绍： SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.