DeepArchitect: Automatically Designing and Training Deep Architectures – implementation –

By May 19, 2017machinelearning



So now that Learning to Learn is becoming more trendy thanks to Sundar Pichai, we now need better methods to look around for models, because who has 800 GPUs [1] ! Here is one with an implementation, woohoo !

In deep learning, performance is strongly affected by the choice of architecture and hyperparameters. While there has been extensive work on automatic hyperparameter optimization for simple spaces, complex spaces such as the space of deep architectures remain largely unexplored. As a result, the choice of architecture is done manually by the human expert through a slow trial and error process guided mainly by intuition. In this paper we describe a framework for automatically designing and training deep models. We propose an extensible and modular language that allows the human expert to compactly represent complex search spaces over architectures and their hyperparameters. The resulting search spaces are tree-structured and therefore easy to traverse. Models can be automatically compiled to computational graphs once values for all hyperparameters have been chosen. We can leverage the structure of the search space to introduce different model search algorithms, such as random search, Monte Carlo tree search (MCTS), and sequential model-based optimization (SMBO). We present experiments comparing the different algorithms on CIFAR-10 and show that MCTS and SMBO outperform random search. In addition, these experiments show that our framework can be used effectively for model discovery, as it is possible to describe expressive search spaces and discover competitive models without much effort from the human expert. Code for our framework and experiments has been made publicly available.


An implementation of DeepArchitect is here: https://github.com/negrinho/deep_architect
[1] ICLR video: Neural Architecture Search with Reinforcement Learning Video starts at 1:08:44 see Learning the structure of learning.



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