神经网络模型的可视化研究进展

Advances in Visualization of Neural Network Models

近年来,深度学习在计算机视觉、语音识别和自然语言处理等各个领域都取得了巨大的成功。深度学习的主要原理是对数据通过层层的特征表达或映射,使数据在模型最高层线性可分或线性可拟合,由于此时的模型参数量相对一般浅层模型较少,从而获得较好的分类或拟合结果。神经网络的应用虽然在许多领域取得了一定的成功,但仍然有很多问题亟待解决。大部分研究人员仍然不清楚神经网络内部是如何从大规模数据中学习到有效的特征表示,神经网络的“黑盒子”特性促进了神经网络可视化技术的发展。可视化技术是以图像可视化的方式对神经网络内部的卷积核以及卷积层所提取到的特征进行分析,帮助理解神经网络每一层是如何提取特征的,从而避免在网络训练过程中的盲目调参和试错。神经网络的可视化对于调整参数有着很好的指导作用,可以使网络结构性能快速达到最优。本文按照以下几方面总结内容:可视化研究的提起、可视化方法、神经网络模型、可视化工具及可视化应用,重点关注了可视化神经网络模型的工具;最后,对该领域存在的难点及未来研究趋势进行了展望。本文通过论述神经网络模型可视化编程工具的发展与应用,旨在为神经网络模型的绘制和对其进行更加深入的了解提供参考和根据。

In recent years, deep learning has achieved great success in various fields such as computer vision, speech recognition and natural language processing. The main principle of deep learning is to express or map the data through layers of features so that the data is linearly separable or linearly fitable at the highest level of the model, which results in better classification or fitting results, because the number of model parameters at this point is relatively small compared to the general shallow model. Although the application of neural networks has been successful in many fields, there are still many problems that need to be solved. Most researchers are still unclear about how neural networks internally learn effective feature representations from large-scale data, and the “black box” nature of neural networks has contributed to the development of neural network visualization techniques. Visualization technique is to analyze the convolutional kernel inside the neural network and the features extracted from the convolutional layers in an image visualization, which helps to understand how features are extracted from each layer of the neural network, thus avoiding blind tuning of parameters and trial and error during the network training process. The visualization of neural networks is a good guide for tuning the parameters, which can make the network structure performance to be optimized quickly. This paper summarizes the content according to the following aspects: the initiation of visual research, visualization methods, neural network models, visualization tools and visualization applications, focusing on the tools of visual neural network models;finally, the difficulties in this field and the future research trends are prospected. By discussing the development and application of visual programming tools for neural network models, this paper aims to provide a reference and basis for the drawing of neural network models and a more indepth understanding of them.