August 2017
Beginner to intermediate
340 pages
8h 42m
English
Content preview from Mastering Machine Learning with Spark 2.x
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Building models and inspecting results
So now that you understand a little about the parameters and the model that we want to run, it's time to go ahead and train and inspect our network:
val dl = new DeepLearning(dlParams) val dlModel = dl.trainModel.get
The code created the DeepLearning model builder and launched it. By default, the launch of trainModel is asynchronous (that is, it never blocks, but returns a job), but it is possible to wait until the end of computation by calling the method get. You can also explore the job progress in UI or even explore the unfinished model by typing getJobs into the Flow UI (see Figure 18).
Figure 18 ...
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