Topic 1 Question 178
A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model's accuracy. The learning rate parameter is specified in the following HPO configuration:
During the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.1. The best result had a learning rate of less than 0.01. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses different learning rates more evenly from the provided range between MinValue and MaxValue.
Which solution provides the MOST accurate result?Modify the HPO configuration as follows:
Select the most accurate hyperparameter configuration form this HPO job.Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job: ✑ [0.01, 0.1] ✑ [0.001, 0.01] ✑ [0.0001, 0.001] Select the most accurate hyperparameter configuration form these three HPO jobs.
Modify the HPO configuration as follows:
Select the most accurate hyperparameter configuration form this training job.Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue. Divide the number of training jobs for each HPO job by three: ✑ [0.01, 0.1] ✑ [0.001, 0.01] [0.0001, 0.001]
Select the most accurate hyperparameter configuration form these three HPO jobs.
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コメント(6)
- 正解だと思う選択肢: C
"Choose logarithmic scaling when you are searching a range that spans several orders of magnitude. For example, if you are tuning a Tune a linear learner model model, and you specify a range of values between .0001 and 1.0 for the learning_rate hyperparameter, searching uniformly on a logarithmic scale gives you a better sample of the entire range than searching on a linear scale would, because searching on a linear scale would, on average, devote 90 percent of your training budget to only the values between .1 and 1.0, leaving only 10 percent of your training budget for the values between .0001 and .1."
based on the above from this link
https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html
C is clearly the answer
👍 6ovokpus2022/06/26 - 正解だと思う選択肢: C👍 5edvardo2022/05/05
"Choose logarithmic scaling when you are searching a range that spans several orders of magnitude. For example, if you are tuning a Tune a linear learner model model, and you specify a range of values between .0001 and 1.0 for the learning_rate hyperparameter, searching uniformly on a logarithmic scale gives you a better sample of the entire range than searching on a linear scale would, because searching on a linear scale would, on average, devote 90 percent of your training budget to only the values between .1 and 1.0, leaving only 10 percent of your training budget for the values between .0001 and .1."
👍 3ovokpus2022/06/26
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