Abstract
The architecture of a neural network (NN) plays a critical role in determining its performance. However, there is no general closed-form function that maps between network structure and accuracy, making the process of architecture design largely heuristic or search-based. In this study, we propose the Architecture Suggesting Neural Network (ASNN), a model designed to learn the relationship between NN architecture and its test accuracy, and to suggest improved architectures accordingly. To train ASNN, we constructed datasets using TensorFlow-based models with varying numbers of layers and nodes. Experimental results were collected for both 2-layer and 3-layer architectures across a grid of configurations, each evaluated with 10 repeated trials to account for stochasticity. Accuracy values were treated as inputs, and architectural parameters as outputs. The trained ASNN was then used iteratively to predict architectures that yield higher performance. In both 2-layer and 3-layer cases, ASNN successfully suggested architectures that outperformed the best results found in the original training data. Repeated prediction and retraining cycles led to the discovery of architectures with improved mean test accuracies, demonstrating the model's capacity to generalize the performance-structure relationship. These results suggest that ASNN provides an efficient alternative to random search for architecture optimization, and offers a promising approach toward automating neural network design. "Parts of the manuscript, including text editing and expression refinement, were supported by OpenAI's ChatGPT. All content was reviewed and verified by the authors."
Abstract (translated)
神经网络（NN）的架构在其性能中扮演着至关重要的角色。然而，没有通用的闭式函数可以映射网络结构与准确度之间的关系，这使得架构设计过程主要依赖于启发法或搜索方法。在这项研究中，我们提出了架构建议神经网络（ASNN），这是一种旨在学习NN架构与其测试精度之间关系，并据此推荐改进后的架构模型。 为了训练ASNN，我们使用基于TensorFlow的具有不同层数和节点数的模型构建了数据集。对于两层和三层架构，在一系列配置网格上收集实验结果，每个配置通过10次重复试验来考虑随机性的影响。将精度值作为输入处理，并将架构参数视为输出。训练后的ASNN随后被迭代地用于预测性能更高的架构。 在二层和三层结构的情况下，ASNN成功提出了优于原始训练数据中最佳结果的架构。反复进行预测和重新训练循环发现具有改进平均测试准确度的架构，展示了该模型概括性能-结构关系的能力。这些结果表明，ASNN为架构优化提供了一种比随机搜索更有效的替代方案，并且在自动化的神经网络设计方面展现出了有前景的方法。 “手稿的部分内容，包括文本编辑和表达润色，得到了OpenAI的ChatGPT的支持。所有内容均已由作者审查并确认。”
URL
https://arxiv.org/abs/2507.20164
