Parameter Grid-searching with Python’s itertools

Python’s Itertools offers a great solution when you want to do a grid-search for optimal hyperparameter values, -or in general generate sets of experiments-.

In the code fragment below we generate experiment settings (key-value pairs stored in dictionaries) for all combinations of batch sizes and learning rates.

import itertools

# General settings
base_settings = {'epochs': 10}

# Grid search
grid = {
    'batch_size': [32, 64, 128],
    'learning_rate': [1E-4, 1E-3, 1E-2]
}

# Loop over al grid search combinations
for values in itertools.product(*grid.values()):
    point = dict(zip(grid.keys(), values))

    # merge the general settings
    settings = {**base_settings, **point}

    print(settings)

output:

{'epochs': 10, 'batch_size': 32, 'learning_rate': 0.0001}
{'epochs': 10, 'batch_size': 32, 'learning_rate': 0.001}
{'epochs': 10, 'batch_size': 32, 'learning_rate': 0.01}
{'epochs': 10, 'batch_size': 64, 'learning_rate': 0.0001}
{'epochs': 10, 'batch_size': 64, 'learning_rate': 0.001}
{'epochs': 10, 'batch_size': 64, 'learning_rate': 0.01}
{'epochs': 10, 'batch_size': 128, 'learning_rate': 0.0001}
{'epochs': 10, 'batch_size': 128, 'learning_rate': 0.001}
{'epochs': 10, 'batch_size': 128, 'learning_rate': 0.01}