  Hyperparameter optimization is a challenging problem in developing deep
neural networks. Decision of transfer layers and trainable layers is a major
task for design of the transfer convolutional neural networks (CNN).
Conventional transfer CNN models are usually manually designed based on
intuition. In this paper, a genetic algorithm is applied to select trainable
layers of the transfer model. The filter criterion is constructed by accuracy
and the counts of the trainable layers. The results show that the method is
competent in this task. The system will converge with a precision of 97% in the
classification of Cats and Dogs datasets, in no more than 15 generations.
Moreover, backward inference according the results of the genetic algorithm
shows that our method can capture the gradient features in network layers,
which plays a part on understanding of the transfer AI models.
