Representing the knowledge learned by neural networks in the form of interpretable rules is a prudent technique to justify the decisions made by neural networks. Heretofore many algorithms exist to extract symbolic rules from neural networks, but among them, a few extract rules from deep neural networks trained using deep learning techniques. So, this paper proposes an algorithm to extract rules from a multi-hidden layer neural network, pre-trained using deep belief network and fine-tuned using back propagation. The algorithm analyzes each node of a layer and extracts knowledge from each layer separately. The process of knowledge extraction from the first hidden layer is different from the other layers. Consecutively, the algorithm combines all the knowledge extracted and refines them to construct a final ruleset consisting of symbolic rules. The algorithm further subdivides the subspace of a rule in the ruleset if it satisfies certain conditions. Results show that the algorithm extracted rules with higher accuracy compared to some existing rule extraction algorithms. Other than accuracy, the efficacy of the extracted rules is also validated with fidelity and various other performance measures.

中文翻译：

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从使用深度信念网络和反向传播训练的神经网络中提取规则

以可解释的规则的形式表示由神经网络学到的知识是一种审慎的技术，可以证明由神经网络做出的决策是合理的。迄今为止，存在许多从神经网络中提取符号规则的算法，但是其中一些算法是从使用深度学习技术训练的深度神经网络中提取规则的。因此，本文提出了一种从多层隐藏神经网络中提取规则的算法，该算法使用深度置信网络进行预训练，并使用反向传播进行精细调整。该算法分析层的每个节点，并分别从每个层提取知识。从第一隐藏层提取知识的过程与其他层不同。连续地，该算法将提取出的所有知识组合在一起，并对它们进行精炼，以构建由符号规则组成的最终规则集。如果满足特定条件，该算法还会在规则集中细分规则的子空间。结果表明，与现有的某些规则提取算法相比，该算法提取的规则精度更高。除了准确性以外，还使用保真度和各种其他性能指标来验证提取规则的有效性。