Mastering Machine Learning with Spark 2.x

Build a 3-layer DNN

H2O exposes slightly different way of building models; however, it is unified among all H2O models. There are three basic building blocks:

• Model parameters: Defines inputs and algorithm specific parameters
• Model builder: Accepts model parameters and produces a model
• Model: Contains model definition but also technical information about model building such as score times or error rates for each iteration

Prior to building our model, we need to construct parameters for the DeepLearning algorithm:

import _root_.hex.deeplearning._ import DeepLearningParameters.Activation val dlParams = new DeepLearningParameters() dlParams._train = trainingHF._key dlParams._valid = testHF._key dlParams._response_column = "label" dlParams._epochs ...