Abstract

Training methods for deep neural networks (DNNs) are analyzed. It is shown that maximizing the likelihood function of the distribution of the input data P(x) in the space of synaptic connections of a restricted Boltzmann machine (RBM) is equivalent to minimizing the cross-entropy (CE) of the network error function and minimizing the total mean squared error (MSE) of the network in the same space using linear neurons. The application of DNNs for the detection and recognition of productmarking is considered.

中文翻译：

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深度神经网络：学习和应用的某些方面

摘要

分析了深度神经网络（DNN）的训练方法。结果表明，在受限玻尔兹曼机器（RBM）的突触连接空间中，使输入数据P（x）的分布的似然函数最大化，等于使网络误差函数的交叉熵（CE）最小，并且使用线性神经元在相同空间中最小化网络的总均方误差（MSE）。考虑了DNN在产品标记检测和识别中的应用。