Neural Optimizer Search with Reinforcement Learning

2017-09-21 · Irwan Bello, Barret Zoph, Vijay Vasudevan, Quoc V. Le

We present an approach to automate the process of discovering optimization methods, with a focus on deep learning architectures. We train a Recurrent Neural Network controller to generate a string in a domain specific language that describes a mathematical update equation based on a list of primitive functions, such as the gradient, running average of the gradient, etc. The controller is trained with Reinforcement Learning to maximize the performance of a model after a few epochs. On CIFAR-10, our method discovers several update rules that are better than many commonly used optimizers, such as Adam, RMSProp, or SGD with and without Momentum on a ConvNet model. We introduce two new optimizers, named PowerSign and AddSign, which we show transfer well and improve training on a variety of different tasks and architectures, including ImageNet classification and Google's neural machine translation system.

📄 PDF Abstract BibTeX arXiv:1709.07417

Code (2)

calclavia/NOS pytorch

carpedm20/ENAS-pytorch pytorch

Tasks

Machine Translation reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Translation

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