A Method to Visualize Deep Convolutional Networks Based on Model Reconstruction
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摘要: 针对深度卷积网络原理分析的问题，该文提出一种基于模型重建的权值可视化方法。首先利用原有的神经网络对测试样本进行前向传播，以获取重建模型所需要的先验信息；然后对原本网络中的部分结构进行修改，使其便于后续的参数计算；再利用正交向量组，逐一地计算重建模型的参数；最后将计算所得的参数按照特定的顺序进行重排列，实现权值的可视化。实验结果表明，对于满足一定条件的深度卷积网络，利用该文所提方法重建的模型在分类过程的前向传播运算中与原模型完全等效，并且可以明显观察到重建后模型的权值所具有的特征，从而分析神经网络实现图像分类的原理。Abstract: A method for visualizing the weights of a reconstructed model is proposed to analyze a deep convolutional network works. Firstly, a specific input is used in the original neural network during the forward propagation to get the prior information for model reconstruction. Then some of the structure of the original network is changed for further parameter calculation. After that, the parameters of the reconstructed model are calculated with a group of orthogonal vectors. Finally, the parameters are put into a special order to make them visualized. Experimental results show that the model reconstructed with the proposed method is totally equivalent to the original model during the forward propagation in the classification process. The feature of the weights of the reconstructed model can be observed clearly and the principle of the neural network can be analyzed with the feature.
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Key words:
- Deep convolutional networks /
- Visualization /
- Model reconstruction
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表 1 两种模型分类输出比较
输出结果 安-26飞机图像 雅克-42飞机图像 原模型 –13.45 –9.22 –8.14 –6.75 –5.63 10.65 11.01 5.93 9.12 7.31 13.42 9.30 8.23 6.83 5.69 –10.81 –11.15 –6.01 –9.22 –7.38 重建后模型 –13.45 –9.22 –8.14 –6.75 –5.63 10.65 11.01 5.93 9.12 7.31 13.42 9.30 8.23 6.83 5.69 –10.81 –11.15 –6.01 –9.22 –7.38 
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