論文紹介:Practical bayesian optimization of machine learning algorithms(nips2012)Practical Bayesian Optimization of Machine Learning Algorithms(NIPS2012)ã®è«–文紹介ã§ã™ã€‚ Gaussian Process ã®ç›´æ„Ÿçš„ãªã‚¤ãƒ¡ãƒ¼ã‚¸ã¨ã€ãã®ä½¿ã‚れ方を解説ã—ã¾ã—ãŸã€‚Read less
Whetlab
import whetlab # Grab the experiment scientist = whetlab.Experiment(name='ImageNet') for i in range(300): # Get a hyperparameter suggestion from Whetlab params = scientist.suggest() # Train your model, do cross-validation, and get the model performance performance = fit_model(**params) # Update Whetlab with the performance scientist.update(params, performance) # That's it! # With a few extra lines
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Welcome to MOE’s documentation! — MOE 0.2.2 documentation
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