Automatic tuning of hyperparameters using Bayesian optimization

Deep learning is a field in artificial intelligence that works well in computer vision, natural language processing and audio recognition. Deep neural network architectures has number of layers to conceive the features well, by itself. The hyperparameter tuning plays a major role in every dataset which has major effect in the performance of the training model. Due to the large dimensionality of data it is impossible to tune the parameters by human expertise. In this paper, we have used the CIFAR-10 Dataset and applied the Bayesian hyperparameter optimization algorithm to enhance the performance of the model. Bayesian optimization can be used for any noisy black box function for hyperparameter tuning. In this work Bayesian optimization clearly obtains optimized values for all hyperparameters which saves time and improves performance. The results also show that the error has been reduced in graphical processing unit than in CPU by 6.2% in the validation. Achieving global optimization in the trained model helps transfer learning across domains as well.