Automatic Design of Deep Networks with Neural Blocks


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.

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  1. 1.

    Baker B, Gupta O, Naik N, Raskar R. 2017. Designing neural network architectures using reinforcement learning. In: ICLR.

  2. 2.

    Bengio Y. Gradient-based optimization of hyperparameters. Neural Comput 2000;12(8):1889–1900.

    CAS  Article  Google Scholar 

  3. 3.

    Bergstra J, Bengio Y. Random search for hyper-parameter optimization. J Mach Learn Res 2012;13:281–305.

    Google Scholar 

  4. 4.

    Bergstra J, Yamins D, Cox DD. 2013. Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. In: ICML, pp 115–123.

  5. 5.

    Botev A, Lever G, Barber D. 2017. Nesterov’s accelerated gradient and momentum as approximations to regularised update descent. In: IJCNN, pp 1899–1903.

  6. 6.

    Cai H, Chen T, Zhang W, Yu Y, Wang J. 2018. Efficient architecture search by network transformation. In: AAAI.

  7. 7.

    Gepperth A, Karaoguz C. A bio-inspired incremental learning architecture for applied perceptual problems. Cogn Comput 2016;8(5):924–934.

    Article  Google Scholar 

  8. 8.

    Glorot X, Bordes A, Bengio Y. 2011. Deep sparse rectifier neural networks. In: AISTATS, pp 315–323.

  9. 9.

    Goodfellow IJ, Warde-farley D, mirza M, courville AC, bengio Y. 2013. Maxout networks. In: ICML, pp 1319–1327.

  10. 10.

    Guo T, Zhang L, Tan X. Neuron pruning-based discriminative extreme learning machine for pattern classification. Cogn Comput 2017;9(4):581–595.

    Article  Google Scholar 

  11. 11.

    He K, Zhang X, Ren S, Sun J. 2015. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: ICCV, pp 1026–1034.

  12. 12.

    He K, Zhang X, Ren S, Sun J. 2016. Deep residual learning for image recognition. In: CVPR, pp 770–778.

  13. 13.

    Huang G, Liu Z, van der Maaten L, Weinberger KQ. 2017. Densely connected convolutional networks. In: CVPR, pp 2261–2269.

  14. 14.

    Huang G, Sun Y, Liu Z, Sedra D, Weinberger KQ. 2016. Deep networks with stochastic depth. In: ECCV, pp 646–661.

  15. 15.

    Ioffe S, Szegedy C. 2015. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML, pp 448–456.

  16. 16.

    Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick RB, Guadarrama S, Darrell T. 2014. Caffe: convolutional architecture for fast feature embedding. In: ACM MM, pp 675–678.

  17. 17.

    Kingma DP, Ba J. 2014. Adam: a method for stochastic optimization. CoRR arXiv:

  18. 18.

    Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In: NeurIPS, pp 1106–1114.

  19. 19.

    Lin LJ. 1993. Reinforcement learning for robots using neural networks. Technical report, DTIC Document.

  20. 20.

    Lin M, Chen Q, Yan S. 2013. Network in network. In: ICLR.

  21. 21.

    Liu C, Zoph B, Neumann M, Shlens J, Hua W, Li L, Fei-fei L, yuille AL, huang J, murphy K. 2018. Progressive neural architecture search. In: ECCV, pp 19–35.

  22. 22.

    Liu H, Simonyan K, Vinyals O, Fernando C, Kavukcuoglu K. 2018. Hierarchical representations for efficient architecture search. In: ICLR.

  23. 23.

    Luo B, Hussain A, Mahmud M, Tang J. Advances in brain-inspired cognitive systems. Cogn Comput 2016;8(5):795–796.

    Google Scholar 

  24. 24.

    Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller MA, Fidjeland A, Ostrovski G, Petersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S, Hassabis D. Human-level control through deep reinforcement learning. Nature 2015;518(7540):529–533.

    CAS  Article  Google Scholar 

  25. 25.

    Pham H, Guan MY, Zoph B, Le QV, Dean J. 2018. Efficient neural architecture search via parameter sharing. In: ICML, pp 4092–4101.

  26. 26.

    Romero A, Ballas N, Kahou SE, Chassang A, Gatta C, Bengio Y. 2014. Fitnets: Hints for thin deep nets. CoRR arXiv:

  27. 27.

    Saxena S, Verbeek J. 2016. Convolutional neural fabrics. In: NeurIPS, pp 4053–4061.

  28. 28.

    Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O. 2017. Proximal policy optimization algorithms. CoRR arXiv:

  29. 29.

    Simonyan K, Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. CoRR arXiv:

  30. 30.

    Snoek J, Larochelle H, Adams RP. 2012. Practical bayesian optimization of machine learning algorithms. In: NeurIPS, pp 2960–2968.

  31. 31.

    Snoek J, Rippel O, Swersky K, Kiros R, Satish N, Sundaram N, Patwary MMA, Prabhat Adams RP. 2015. Scalable bayesian optimization using deep neural networks. In: ICML, pp 2171–2180.

  32. 32.

    Srivastava RK, Greff K, Schmidhuber J. 2015. Highway networks. CoRR arXiv:

  33. 33.

    Stanley KO, D’Ambrosio DB, Gauci J. A hypercube-based encoding for evolving large-scale neural networks. Artif Life 2009;15(2):185–212.

    Article  Google Scholar 

  34. 34.

    Stanley KO, Miikkulainen R. Evolving neural networks through augmenting topologies. Evol Comput. 2002:99–127.

    Article  Google Scholar 

  35. 35.

    Suganuma M, Shirakawa S, Nagao T. 2017. A genetic programming approach to designing convolutional neural network architectures. In: GECCO, pp 497–504.

  36. 36.

    Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. 2015. Going deeper with convolutions. In: CVPR, pp 1–9.

  37. 37.

    Taylor JG. Cognitive computation. Cogn Comput 2009;1(1):4–16.

    Article  Google Scholar 

  38. 38.

    Williams RJ. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach Learn 1992;8:229–256.

    Google Scholar 

  39. 39.

    Zhang S, Huang K, Zhang R, Hussain A. Learning from few samples with memory network. Cogn Comput 2018;10 (1):15–22.

    Article  Google Scholar 

  40. 40.

    Zhao F, Zeng Y, Wang G, Bai J, Xu B. A brain-inspired decision making model based on top-down biasing of prefrontal cortex to basal ganglia and its application in autonomous UAV explorations. Cogn Comput 2018;10(2):296–306.

    Article  Google Scholar 

  41. 41.

    Zhong G, Yan S, Huang K, Cai Y, Dong J. Reducing and stretching deep convolutional activation features for accurate image classification. Cogn Comput 2018;10(1):179–186.

    Article  Google Scholar 

  42. 42.

    Zhong Z, Yan J, Liu C. 2018. Practical block-wise neural network architecture generation. In: CVPR.

  43. 43.

    Zoph B, Le QV. 2017. Neural architecture search with reinforcement learning. In: ICML.

  44. 44.

    Zoph B, Vasudevan V, Shlens J, Le QV. 2018. Learning transferable architectures for scalable image recognition. In: CVPR.

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This work was supported by the National Key R&D Program of China under Grant 2016YFC1401004, the National Natural Science Foundation of China (NSFC) under Grant No. 41706010 and 61876155, the Science and Technology Program of Qingdao under Grant No. 17-3-3-20-nsh, the CERNET Innovation Project under Grant No. NGII20170416, and the Fundamental Research Funds for the Central Universities of China. In addition, we would like to thank Tao Li for his helpful comments and discussions. We also would like to thank the editor and anonymous reviewers for their helpful reviews.

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Correspondence to Guoqiang Zhong.

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Zhong, G., Jiao, W., Gao, W. et al. Automatic Design of Deep Networks with Neural Blocks. Cogn Comput 12, 1–12 (2020).

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  • Automatic deep networks design
  • Reinforcement learning
  • Deep convolutional neural networks
  • Neural blocks
  • Image classification