【摘要】 目前的神经网络结构自动化设计方法主要对所设计神经网络结构的预测准确率进行优化。然而,实际应用中经常要求所设计的神经网络结构满足特定的代价约束,如内存占用、推断时间和训练时间等。该文提出了一种新的限定代价下的神经网络结构自动化设计方法,选取内存占用、推断时间和训练时间三类代表性代价在CIFAR10数据集上进行了实验,并与现有方法进行了对比分析。该方法获得了满足特定代价约束的高准确率的卷积神经网络结构,可优化的代价种类比现有方法更多。更多还原

【Abstract】 Recently, automated neural network architecture design(neural architecture search) has yielded many significant achievements. Improving the prediction accuracy of the neural network is the primary goal.However, besides the prediction accuracy, other types of cost including memory consumption, inference time, and training time are also very important when implementing the neural network. In practice, such types of cost are often bounded by thresholds. Current neural architecture search method with budgeted cost constraints can only optimize some specific types of the cost. In this paper, we propose budgeted efficient neural architecture search(B-ENAS) to optimize more types of cost. The experimental results on the well-adopted CIFAR 10 dataset show that B-ENAS can learn convolutional neural network architectures with high accuracy under different cost constraints.更多还原