![]() To learn more about Bayesian hyperparameter optimization, refer to the slides from Roger Grosse, professor and researcher at the University of Toronto. It may sound complicated, but it’s quite easy once you dig into the code.Īdditionally, if you are interested in learning more about the Hyperband algorithm, be sure to read Li et al.’s 2018 publication, Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. The keras tuner package takes care of the rest, running multiple trials until we converge on the best set of hyperparameters.We then define an instance of either Hyperband, RandomSearch, or BayesianOptimization.As we implement our model architecture, we define what ranges we want to search over for a given parameter (e.g., # of filters in our first CONV layer, # of filters in the second CONV layer, etc.). ![]() Libraries such as keras tuner make it dead simple to implement hyperparameter optimization into our training scripts in an organic manner: While this method worked well (and gave us a nice boost in accuracy), the code wasn’t necessarily “pretty.”Īnd more importantly, it doesn’t make it easy for us to tune the “internal” parameters of a model architecture (e.g., the number of filters in a CONV layer, stride size, size of a POOL, dropout rate, etc.). Last week, you learned how to use scikit-learn’s hyperparameter searching functions to tune the hyperparameters of a basic feedforward neural network (including batch size, the number of epochs to train for, learning rate, and the number of nodes in a given layer). What is Keras Tuner, and how can it help us automatically tune hyperparameters?įigure 1: Using Keras Tuner to automatically tune the hyperparameters to your Keras and TensorFlow models ( image source). We’ll wrap up this tutorial with a discussion of our results. ![]()
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