model <- h2o.deeplearning(x = 2:785, # column numbers for predictors y = 1, # column number for label data = train_h2o, # data in H2O format activation = "TanhWithDropout", # or 'Tanh' input_dropout_ratio = 0.2, # % of inputs dropout hidden_dropout_ratios = c(0.5,0.5,0.5), # % for nodes dropout balance_classes = TRUE, hidden = c(50,50,50), # three layers of 50 nodes epochs = 100) # max. no. of epochs