Which two parameter expressions should you use? Each correct answer presents part of the solution.

Last Updated on October 21, 2021 by Admin

You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.

You must use Hyperdrive to try combinations of the following hyperparameter values:

– learning_rate: any value between 0.001 and 0.1
– batch_size: 16, 32, or 64

You need to configure the search space for the Hyperdrive experiment.

Which two parameter expressions should you use? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

  • a choice expression for learning_rate
  • a uniform expression for learning_rate
  • a normal expression for batch_size
  • a choice expression for batch_size
  • a uniform expression for batch_size
Explanation:

B: Continuous hyperparameters are specified as a distribution over a continuous range of values. Supported distributions include:
– uniform(low, high) – Returns a value uniformly distributed between low and high

D: Discrete hyperparameters are specified as a choice among discrete values. choice can be:
– one or more comma-separated values
– a range object
– any arbitrary list object

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