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Azure Machine Learning Serviceを使用して、ニューラルネットワーク分類モデルのハイパーパラメーター探索を自動化しています。
次の要件に従ってランダムサンプリングを使用してハイパーパラメーターを自動的に調整するには、ハイパーパラメーター空間を定義する必要があります。
*学習率は、平均値が10、標準偏差が3の正規分布から選択する必要があります。
*バッチサイズは16、32、64でなければなりません。
*維持確率は、0.05から0.1の範囲の均一な分布から選択された値でなければなりません。
Azure Machine Learning ServiceのPython APIのparam_samplingメソッドを使用する必要があります。
コードセグメントをどのように完成させる必要がありますか？ 回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。
Answer:
Explanation:
Explanation:
In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.
Example:
from azureml.train.hyperdrive import RandomParameterSampling
param_sampling = RandomParameterSampling( {
"learning_rate": normal(10, 3),
"keep_probability": uniform(0.05, 0.1),
"batch_size": choice(16, 32, 64)
}
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters