neural_compressor.experimental.strategy.bayesian
The Bayesian tuning strategy.
Module Contents
Classes
The Bayesian tuning strategy. |
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Holds the param-space coordinates (X) and target values (Y). |
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The class for bayesian optimization. |
Functions
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Find the maximum of the acquisition function parameters. |
- class neural_compressor.experimental.strategy.bayesian.BayesianTuneStrategy(model, conf, q_dataloader, q_func=None, eval_dataloader=None, eval_func=None, dicts=None, q_hooks=None)[source]
The Bayesian tuning strategy.
- neural_compressor.experimental.strategy.bayesian.acq_max(ac, gp, y_max, bounds, random_seed, n_warmup=10000, n_iter=10)[source]
Find the maximum of the acquisition function parameters.
- Parameters:
ac – The acquisition function object that return its point-wise value.
gp – A gaussian process fitted to the relevant data.
y_max – The current maximum known value of the target function.
bounds – The variables bounds to limit the search of the acq max.
random_seed – instance of np.RandomState random number generator
n_warmup – number of times to randomly sample the acquisition function
n_iter – number of times to run scipy.minimize
- Returns:
The arg max of the acquisition function.
- Return type:
x_max
- class neural_compressor.experimental.strategy.bayesian.TargetSpace(pbounds, random_seed=9527)[source]
Holds the param-space coordinates (X) and target values (Y).
Allows for constant-time appends while ensuring no duplicates are added.
- class neural_compressor.experimental.strategy.bayesian.BayesianOptimization(pbounds, random_seed=9527, verbose=2)[source]
The class for bayesian optimization.
This class takes the parameters bounds in order to find which values for the parameters yield the maximum value using bayesian optimization.