  Convolutional Neural Network (CNN) has gained state-of-the-art results in
many pattern recognition and computer vision tasks. However, most of the CNN
structures are manually designed by experienced researchers. Therefore, auto-
matically building high performance networks becomes an important problem. In
this paper, we introduce the idea of using DNA computing algorithm to
automatically learn high-performance architectures. In DNA computing algorithm,
we use short DNA strands to represent layers and long DNA strands to represent
overall networks. We found that most of the learned models perform similarly,
and only those performing worse during the first runs of training will perform
worse finally than others. The indicates that: 1) Using DNA computing algorithm
to learn deep architectures is feasible; 2) Local minima should not be a
problem of deep networks; 3) We can use early stop to kill the models with the
bad performance just after several runs of training. In our experiments, an
accuracy 99.73% was obtained on the MNIST data set and an accuracy 95.10% was
obtained on the CIFAR-10 data set.
