wf_psf.training.train module

Train.

A module which defines the classes and methods to manage training of the psf model.

Author:: Jennifer Pollack <jennifer.pollack@cea.fr>

class wf_psf.training.train.TrainingParamsHandler(training_params)[source]

Bases: object

Training Parameters Handler.

A class to handle training parameters accessed:

Parameters:

training_params (Recursive Namespace object) – Recursive Namespace object containing training input parameters

Attributes:

id_name: ID Name.
learning_rate_non_params: Non-parametric Model Learning Rate.
learning_rate_params: Parametric Model Learning Rate.
model_name: PSF Model Name.
model_params: PSF Model Params.
multi_cycle_params: Training Multi Cycle Parameters.
n_epochs_non_params: Number of Epochs for Non-parametric PSF model.
n_epochs_params: Number of Epochs for Parametric PSF model.
total_cycles: Total Number of Cycles.
training_hparams: Training Hyperparameters.

property id_name

ID Name.

Set unique ID name.

Returns:: id_name – A unique ID.
Return type:: str

property learning_rate_non_params

Non-parametric Model Learning Rate.

Set learning rate for non-parametric PSF model.

Returns:: learning_rate_non_params – List containing learning rate for non-parametric PSF model
Return type:: list

property learning_rate_params

Parametric Model Learning Rate.

Set learning rate for parametric PSF model.

Returns:: learning_rate_params – List containing learning rate for parametric PSF model
Return type:: list

property model_name

PSF Model Name.

Set model_name.

Returns:: model_name – Name of PSF model
Return type:: str

property model_params

PSF Model Params.

Set PSF model training parameters

Returns:: model_params – Recursive Namespace object storing PSF model parameters
Return type:: Recursive Namespace object

property multi_cycle_params

Training Multi Cycle Parameters.

Set training multi cycle parameters

Returns:: multi_cycle_params – Recursive Namespace object storing training multi-cycle parameters
Return type:: Recursive Namespace object

property n_epochs_non_params

Number of Epochs for Non-parametric PSF model.

Set the number of epochs for training non-parametric PSF model.

Returns:: n_epochs_non_params – List of number of epochs for training non-parametric PSF model.
Return type:: list

property n_epochs_params

Number of Epochs for Parametric PSF model.

Set the number of epochs for training parametric PSF model.

Returns:: n_epochs_params – List of number of epochs for training parametric PSF model.
Return type:: list

property total_cycles

Total Number of Cycles.

Set total number of cycles for training.

Returns:: total_cycles – Total number of cycles for training
Return type:: int

property training_hparams

Training Hyperparameters.

Set training hyperparameters

Returns:: training_hparams – Recursive Namespace object storing training hyper parameters
Return type:: Recursive Namespace object

wf_psf.training.train.filepath_chkp_callback(checkpoint_dir, model_name, id_name, current_cycle)[source]

wf_psf.training.train.get_gpu_info()[source]

Get GPU Information.

A function to return GPU device name.

Returns:: device_name – Name of GPU device
Return type:: str

wf_psf.training.train.setup_training()[source]

Setup Training.

A function to setup training.

wf_psf.training.train.train(training_params, training_data, test_data, checkpoint_dir, optimizer_dir, psf_model_dir)[source]

Train.

A function to train the psf model.

Parameters:

training_params (Recursive Namespace object) – Recursive Namespace object containing the training parameters
training_data (obj) – TrainingDataHandler object containing the training data parameters
test_data (object) – TestDataHandler object containing the test data parameters
checkpoint_dir (str) – Absolute path to checkpoint directory
optimizer_dir (str) – Absolute path to optimizer history directory
psf_model_dir (str) – Absolute path to psf model directory