SigOpt for ML: TensorFlow ConvNets on a Budget with Bayesian Optimization

In this post on integrating SigOpt with machine learning frameworks, we will show you how to use SigOpt and TensorFlow to efficiently search for an optimal configuration of a convolutional neural network (CNN). There are a large number of tunable parameters associated with defining and training deep neural networks ( Bergstra [1]) and SigOpt accelerates searching through these settings to find optimal configurations. This search is typically a slow and expensive process, especially when using standard techniques like grid or random search, as evaluating each configuration can take multiple hours. SigOpt finds good combinations far more efficiently than these standard methods by employing an ensemble of state-of-the-art Bayesian optimization techniques, allowing users to arrive at the best models faster and cheaper. In this example, we consider the same optical character recognition task of the SVHN dataset as discussed in a previous post.