Gradient-based Hyperparameter Optimization through Reversible Learning

Proceedings of the 32nd International Conference on Machine Learning (2015)
arXiv:1502.03492 [stat.ML] | PDF | Google Doc | Code | Google Scholar | BibTex | EndNote


Tuning hyperparameters of learning algorithms is hard because gradients are usually unavailable. We compute exact gradients of cross-validation performance with respect to all hyperparameters by chaining derivatives backwards through the entire training procedure. These gradients allow us to optimize thousands of hyperparameters, including step-size and momentum schedules, weight initialization distributions, richly parameterized regularization schemes, and neural network architectures. We compute hyperparameter gradients by exactly reversing the dynamics of stochastic gradient descent with momentum.


deep learning, neural networks