  Real artificial intelligence always has been focused on by many machine
learning researchers, especially in the area of deep learning. However deep
neural network is hard to be understood and explained, and sometimes, even
metaphysics. The reason is, we believe that: the network is essentially a
perceptual model. Therefore, we believe that in order to complete complex
intelligent activities from simple perception, it is necessary to con-struct
another interpretable logical network to form accurate and reasonable responses
and explanations to external things. Researchers like Bolei Zhou and Quanshi
Zhang have found many explanatory rules for deep feature extraction aimed at
the feature extraction stage of convolution neural network. However, although
researchers like Marco Gori have also made great efforts to improve the
interpretability of the fully connected layers of the network, the problem is
also very difficult. This paper firstly analyzes its reason. Then a method of
constructing logical network based on the fully connected layers and extracting
logical relation between input and output of the layers is proposed. The game
process between perceptual learning and logical abstract cognitive learning is
implemented to improve the interpretable performance of deep learning process
and deep learning model. The benefits of our approach are illustrated on
benchmark data sets and in real-world experiments.
