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Microsoft

DP-100 · Question #197

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 hype

The correct answer is B. a uniform expression for learning_rate D. a choice expression for batch_size. 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 v

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Question

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.

Options

  • Aa choice expression for learning_rate
  • Ba uniform expression for learning_rate
  • Ca normal expression for batch_size
  • Da choice expression for batch_size
  • Ea uniform expression for batch_size

How the community answered

(50 responses)
  • A
    2% (1)
  • B
    82% (41)
  • C
    10% (5)
  • E
    6% (3)

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 any arbitrary list object https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

Topics

#Azure Machine Learning#Hyperparameter Tuning#Hyperdrive#Search Space

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