Which two parameter expressions should you use?

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 . a choice expression for learning_rate
B . a uniform expression for learning_rate
C . a normal expression for batch_size
D . a choice expression for batch_size
E . a uniform expression for batch_size

View Answer

Answer: B,D

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

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters