R Deep Learning Cookbook

How to do it...

The section will focus on optimizing hyper parameters in H2O using grid searches.

1. In our case, we will optimize for the activation function, the number of hidden layers (along with the number of neurons in each layer), epochs, and regularization lambda (l1 and l2):

# Perform hyper parameter tuningactivation_opt <- c("Rectifier","RectifierWithDropout", "Maxout","MaxoutWithDropout")hidden_opt <- list(5, c(5,5))epoch_opt <- c(10,50,100)l1_opt <- c(0,1e-3,1e-4)l2_opt <- c(0,1e-3,1e-4)hyper_params <- list(activation = activation_opt,                     hidden = hidden_opt,                     epochs = epoch_opt,                     l1 = l1_opt,                     l2 = l2_opt)

2. The following search criteria have been set to perform a grid search. Adding to the following list, one can also specify the ...