model_trainer – BNN Training Module

This script will initialize and train a BNN model on a strong lensing image dataset.

Examples

python -m model_trainer configs/t1.json

ovejero.model_trainer.config_checker(cfg)

Check that configuration file meets ovejero requirements. Throw an error if configuration file is invalid.

Parameters: cfg: The dictionary attained from reading the json config.

ovejero.model_trainer.get_normed_pixel_scale(cfg, pixel_scale)

Return a dictionary with the pixel scale normalized according to the normalization of each shift parameter.

Parameters:

• cfg (dict) – The dictionary attained from reading the json config file.
• pixel_scale (float) – The pixel scale used for the original images.

Returns:

A dictionary of the pixel scales renormalized in the same way as the shift parameters.

Return type:

(dict)

ovejero.model_trainer.load_config(config_path)

Load a configuration file from the path and check that it meets the requirements.

Parameters:config_path (str) – The path to the config file to be loaded Returns:A dictionary object with the config file. Return type:(dict)

ovejero.model_trainer.main()

Initializes and trains a BNN network. Path to config file are read from command line arguments.

ovejero.model_trainer.model_loss_builder(cfg, verbose=False)

Build a model according to the specifications in configuration dictionary and return both the initialized model and the loss function.

Parameters:

• cfg (dict) – The dictionary attained from reading the json config file.
• verbose (bool) – If True, will be verbose as model is built.

Returns:

A bnn model of the type specified in config and a callable function to construct the tesnorflow graph for the loss.

Return type:

(tf.keras.model, function)

ovejero.model_trainer.prepare_tf_record(cfg, root_path, tf_record_path, final_params, train_or_test)

Perpare the tf record using the config file values.

Parameters:

• cfg (dict) – The dictionary attained from reading the json config file.
• root_path (str) – The root path that will contain all of the data including the lens parameters, the npy files, and the TFRecord.
• tf_record_path (str) – The path where the TFRecord will be saved.
• final_params ([str,..]) – The parameters we expect to be in the final set of lens parameters.
• train_or_test (string) – If test, the normalizations will be saved. If train, the training normalizations will be used.