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.
Apart from the genetic algorithms, Bergstra and Bengio @cite_14 have proposed random search for hyper-parameter optimization, and stated that randomly chosen trials are more efficient for hyper-parameter optimization than trials on a grid. Yan and Zhang @cite_4 optimized architectures' width and height with growing running time budget through submodularity and suparmodularity.
@cite_14 @cite_4
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Grid search and manual search are the most widely used strategies for hyper-parameter optimization. This paper shows empirically and theoretically that randomly chosen trials are more efficient for hyper-parameter optimization than trials on a grid. Empirical evidence comes from a comparison with a large previous study that used grid search and manual search to configure neural networks and deep belief networks. Compared with neural networks configured by a pure grid search, we find that random search over the same domain is able to find models that are as good or better within a small fraction of the computation time. Granting random search the same computational budget, random search finds better models by effectively searching a larger, less promising configuration space. Compared with deep belief networks configured by a thoughtful combination of manual search and grid search, purely random search over the same 32-dimensional configuration space found statistically equal performance on four of seven data sets, and superior performance on one of seven. A Gaussian process analysis of the function from hyper-parameters to validation set performance reveals that for most data sets only a few of the hyper-parameters really matter, but that different hyper-parameters are important on different data sets. This phenomenon makes grid search a poor choice for configuring algorithms for new data sets. Our analysis casts some light on why recent "High Throughput" methods achieve surprising success--they appear to search through a large number of hyper-parameters because most hyper-parameters do not matter much. We anticipate that growing interest in large hierarchical models will place an increasing burden on techniques for hyper-parameter optimization; this work shows that random search is a natural baseline against which to judge progress in the development of adaptive (sequential) hyper-parameter optimization algorithms. Deep learning models' architectures, including depth and width, are key factors influencing models' performance, such as test accuracy and computation time. This paper solves two problems: given computation time budget, choose an architecture to maximize accuracy, and given accuracy requirement, choose an architecture to minimize computation time. We convert this architecture optimization into a subset selection problem. With accuracy's submodularity and computation time's supermodularity, we propose efficient greedy optimization algorithms. The experiments demonstrate our algorithm's ability to find more accurate models or faster models. By analyzing architecture evolution with growing time budget, we discuss relationships among accuracy, time and architecture, and give suggestions on neural network architecture design.