autoaug.autoaugment_learners.GenLearner

class autoaug.autoaugment_learners.GenLearner(num_sub_policies=5, p_bins=11, m_bins=10, exclude_method=[], learning_rate=0.1, max_epochs=inf, early_stop_num=20, batch_size=8, toy_size=1, num_offspring=2)

Genetic Algorithm learner

Parameters

• num_sub_policies (int, optional) – number of subpolicies per policy. Defaults to 5.

• p_bins (int, optional) – number of bins we divide the interval [0,1] for probabilities. e.g. (0.0, 0.1, … 1.0) Defaults to 11.

• m_bins (int, optional) – number of bins we divide the magnitude space. Defaults to 10.

• exclude_method (list, optional) – list of names(:type:str) of image operations the user wants to exclude from the search space. Defaults to [].

• learning_rate (float, optional) – child_network training parameter. Defaults to 1e-1.

• max_epochs (Union[int, float], optional) – child_network training parameter. Defaults to float(‘inf’).

• early_stop_num (int, optional) – child_network training parameter. Defaults to 20.

• batch_size (int, optional) – child_network training parameter. Defaults to 32.

• toy_size (int, optional) – child_network training parameter. ratio of original dataset used in toy dataset. Defaults to 0.1.

• num_offsprings (int, optional) – Defaults to 1

Examples

from autoaug.autoaugment_learners.GenLearner import GenLearner evo_learner = GenLearner()

learn(train_dataset, test_dataset, child_network_architecture, iterations=100)

Generates policies through a genetic algorithm.

Parameters

• torchvision.dataset (test_dataset ->) –

• torchvision.dataset –

• -> (child_network_architecture) –

• int (iterations ->) – number of iterations to run the instance for