In recent years, deep neural networks (DNNs) have achieved great successes in many areas, such as cognitive computation, pattern recognition, and computer vision. Although many hand-crafted deep networks have been proposed in the literature, designing a well-behaved neural network for a specific application requires high-level expertise yet. Hence, the automatic architecture design of DNNs has become a challenging and important problem. In this paper, we propose a new reinforcement learning method, whose action policy is to select neural blocks and construct deep networks. We define the action search space with three types of neural blocks, i.e., dense block, residual block, and inception-like block. Additionally, we have also designed several variants for the residual and inception-like blocks. The optimal network is automatically learned by a Q-learning agent, which is iteratively trained to generate well-performed deep networks. To evaluate the proposed method, we have conducted experiments on three datasets, MNIST, SVHN, and CIFAR-10, for image classification applications. Compared with existing hand-crafted and auto-generated neural networks, our auto-designed neural network delivers promising results. Moreover, the proposed reinforcement learning algorithm for deep networks design only runs on one GPU, demonstrating much higher efficiency than most of the previous deep network search approaches.

中文翻译：

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具有神经块的深层网络的自动设计

近年来，深度神经网络（DNN）在许多领域都取得了巨大的成功，例如认知计算，模式识别和计算机视觉。尽管在文献中已经提出了许多手工的深度网络，但是为特定的应用设计行为良好的神经网络还需要高级的专业知识。因此，DNN的自动体系结构设计已成为一个具有挑战性的重要问题。在本文中，我们提出了一种新的强化学习方法，其作用策略是选择神经块并构建深度网络。我们用三种类型的神经块定义动作搜索空间，即密集块，残差块和类似初始块。此外，我们还为残差块和类似初始块设计了几种变体。最佳网络由Q学习代理自动学习，该代理经过反复训练以生成性能良好的深度网络。为了评估该方法，我们针对图像分类应用对MNIST，SVHN和CIFAR-10这三个数据集进行了实验。与现有的手工制作和自动生成的神经网络相比，我们的自动设计的神经网络可提供令人鼓舞的结果。此外，所提出的用于深度网络设计的强化学习算法仅在一个GPU上运行，证明其效率比大多数以前的深度网络搜索方法高得多。与现有的手工制作和自动生成的神经网络相比，我们的自动设计的神经网络可提供令人鼓舞的结果。此外，所提出的用于深度网络设计的强化学习算法仅在一个GPU上运行，证明其效率比大多数以前的深度网络搜索方法高得多。与现有的手工制作和自动生成的神经网络相比，我们的自动设计的神经网络可提供令人鼓舞的结果。此外，所提出的用于深度网络设计的强化学习算法仅在一个GPU上运行，证明其效率比大多数以前的深度网络搜索方法高得多。