Contents
Which is a hyperparameter tuning method?
Grid search is arguably the most basic hyperparameter tuning method. With this technique, we simply build a model for each possible combination of all of the hyperparameter values provided, evaluating each model, and selecting the architecture which produces the best results.
What are the hyperparameters that you can tune using SGD?
The hyperparameters that can be optimized in SGD are learning rate , momentum , decay and nesterov . Learning rate controls the weight at the end of each batch, and momentum controls how much to let the previous update influence the current weight update.
How is the Gaussian process used in hyperparameter optimization?
The Gaussian process is modeling the probability of our model’s performance ( f) as a function of the hyperparameters ( x ), and so at any value of x, it gives us a normal distribution for its prediction of the value of f:
How to tune hyperparameter tuning with Bayesian optimization?
Now Lets get to the Fun Part, HyperParameter Tuning. (If you followed along with the first part of the article, I found this part works best if you restart your kernel and skip the code for the baseline NN.) We start by importing functions from sci-kit optimize and Keras. Creating our search parameters. “dim_” short for dimension.
What is the error of the Gaussian process?
The gray dots show the error for hyperparameters which have been tried. Also shown is the Gaussian process’s estimate as to the error over all hyperparameter combinations (blue line) and its 1 sigma confidence interval (shaded area).
How does a Gaussian process model the dependent variable Y?
A Gaussian process models the dependent variable y (in our case, the cross-validated performance) as being drawn from a N -dimensional multivariate normal distribution: We’ll just normalize the data such that it has a mean of 0, and use m u = 0.