  Logic reasoning is a significant ability of human intelligence and also an
important task in artificial intelligence. The existing logic reasoning
methods, quite often, need to design some reasoning patterns beforehand. This
has led to an interesting question: can logic reasoning patterns be directly
learned from given data? The problem is termed as a data concept logic. In this
study, a learning logic task from images, called a LiLi task, first is
proposed. This task is to learn and reason the logic relation from images,
without presetting any reasoning patterns. As a preliminary exploration, we
design six LiLi data sets (Bitwise And, Bitwise Or, Bitwise Xor, Addition,
Subtraction and Multiplication), in which each image is embedded with a n-digit
number. It is worth noting that a learning model beforehand does not know the
meaning of the n-digit numbers embedded in images and the relation between the
input images and the output image. In order to tackle the task, in this work we
use many typical neural network models and produce fruitful results. However,
these models have the poor performances on the difficult logic task. For
furthermore addressing this task, a novel network framework called a divide and
conquer model by adding some label information is designed, achieving a high
testing accuracy.
