  The choice of parameters, and the design of the network architecture are
important factors affecting the performance of deep neural networks. However,
there has not been much work on developing an established and systematic way of
building the structure and choosing the parameters of a neural network, and
this task heavily depends on trial and error and empirical results. Considering
that there are many design and parameter choices, such as the number of neurons
in each layer, the type of activation function, the choice of using drop out or
not, it is very hard to cover every configuration, and find the optimal
structure. In this paper, we propose a novel and systematic method that
autonomously and simultaneously optimizes multiple parameters of any given deep
neural network by using a generative adversarial network (GAN). In our proposed
approach, two different models compete and improve each other progressively
with a GAN-based strategy. Our proposed approach can be used to autonomously
refine the parameters, and improve the accuracy of different deep neural
network architectures. Without loss of generality, the proposed method has been
tested with three different neural network architectures, and three very
different datasets and applications. The results show that the presented
approach can simultaneously and successfully optimize multiple neural network
parameters, and achieve increased accuracy in all three scenarios.
