You have a multi-class image classification deep learning model that uses a set of labeled photographs. You create the following code to select hyperparameter values when training the model.

Last Updated on October 21, 2021 by Admin

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You have a multi-class image classification deep learning model that uses a set of labeled photographs. You create the following code to select hyperparameter values when training the model.

DP-100 Designing and Implementing a Data Science Solution on Azure Part 06 Q05 100

DP-100 Designing and Implementing a Data Science Solution on Azure Part 06 Q05 100

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

DP-100 Designing and Implementing a Data Science Solution on Azure Part 06 Q05 101 Question

DP-100 Designing and Implementing a Data Science Solution on Azure Part 06 Q05 101 Question

DP-100 Designing and Implementing a Data Science Solution on Azure Part 06 Q05 101 Answer

DP-100 Designing and Implementing a Data Science Solution on Azure Part 06 Q05 101 Answer

Explanation:

Box 1: Yes
Hyperparameters are adjustable parameters you choose to train a model that govern the training process itself. Azure Machine Learning allows you to automate hyperparameter exploration in an efficient manner, saving you significant time and resources. You specify the range of hyperparameter values and a maximum number of training runs. The system then automatically launches multiple simultaneous runs with different parameter configurations and finds the configuration that results in the best performance, measured by the metric you choose. Poorly performing training runs are automatically early terminated, reducing wastage of compute resources. These resources are instead used to explore other hyperparameter configurations.

Box 2: Yes
uniform(low, high) – Returns a value uniformly distributed between low and high

Box 3: No
Bayesian sampling does not currently support any early termination policy.

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