Convolutional neural networks (CNNs) are a representative class of deep learning algorithms including convolutional computation that perform translation-invariant classification of input data based on their hierarchical architecture. However, classical convolutional neural network learning methods use the steepest descent algorithm for training, and the learning performance is greatly influenced by the initial weight settings of the convolutional and fully connected layers, requiring re-tuning to achieve better performance under different model structures and data. Combining the strengths of the simulated annealing algorithm in global search, we propose applying it to the hyperparameter search process in order to increase the effectiveness of convolutional neural networks (CNNs). In this paper, we introduce SA-CNN neural networks for text classification tasks based on Text-CNN neural networks and implement the simulated annealing algorithm for hyperparameter search. Experiments demonstrate that we can achieve greater classification accuracy than earlier models with manual tuning, and the improvement in time and space for exploration relative to human tuning is substantial.

翻译：卷积神经网络（CNN）是深度学习算法中的代表性类别，其包含卷积计算，能够基于层次化架构对输入数据进行平移不变分类。然而，经典卷积神经网络学习方法采用最速下降算法进行训练，且学习性能受卷积层与全连接层初始权重设定的影响较大，在不同模型结构与数据条件下需重新调参才能获得更优性能。通过结合模拟退火算法在全局搜索方面的优势，我们提出将其应用于超参数搜索过程，以提高卷积神经网络的效能。本文基于Text-CNN神经网络，提出了用于文本分类任务的SA-CNN神经网络，并实现了模拟退火算法的超参数搜索。实验表明，与早期人工调参模型相比，我们能够获得更高的分类精度，且在探索的时间与空间效率上相较于人工调参有显著提升。