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Use the --style random parameter to apply a random 32 base styles Style Tuner code to your prompt. You can also use --style random-16, --style random-64 or --style random-128 to use random results from other lengths of tuners.
--random simulates Style Tuner code with random selections chosen for 75% of the image pairs. You can adjust this percentage by adding a number to the end of the --random parameter. For example, --style random-32-15 simulates a 32-pair tuner with 15% of the image pairs selected, --style random-128-80 simulates a 128-pair tuner with 80% of the image pairs selected.
Think of tune() here as a placeholder. After the tuning process, we will select a single numeric value for each of these hyperparameters. For now, we specify our parsnip model object and identify the hyperparameters we will tune().
The function grid_regular() is from the dials package. It chooses sensible values to try for each hyperparameter; here, we asked for 5 of each. Since we have two to tune, grid_regular() returns 5 \(\times\) 5 = 25 different possible tuning combinations to try in a tidy tibble format.
We leave it to the reader to explore whether you can tune a different decision tree hyperparameter. You can explore the reference docs, or use the args() function to see which parsnip object arguments are available:
Tune is a Python library for experiment execution and hyperparameter tuning at any scale.You can tune your favorite machine learning framework (PyTorch, XGBoost, Scikit-Learn, TensorFlow and Keras, and more) by running state of the art algorithms such as Population Based Training (PBT) and HyperBand/ASHA.Tune further integrates with a wide range of additional hyperparameter optimization tools, including Ax, BayesOpt, BOHB, and Optuna.
Auto-Tune automatically tunes this threshold, typically between 5-15%, based on the amount of JVM that is currently occupied on the system. For example, if JVM memory pressure is high, Auto-Tune might reduce the threshold to 5%, at which point you might see more rejections until the cluster stabilizes and the threshold increases.
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The ability to tune models is important. 'tune' contains functions and classes to be used in conjunction with other 'tidymodels' packages for finding reasonable values of hyper-parameters in models, pre-processing methods, and post-processing steps.
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