hookeai.model_architectures.procedures.model_training¶

Procedures associated to model training.

Functions¶

save_training_state: Save model and optimizer states at given training epoch.
save_loss_history: Save training process loss history record.
read_loss_history_from_file: Read training process loss history from loss history record file.
read_lr_history_from_file: Read training learning rate history from loss history record file.
write_training_summary_file: Write summary data file for model training process.
plot_training_loss_history: Plot model training process loss history.
plot_training_loss_and_lr_history: Plot model training process loss and learning rate histories.
plot_model_parameters_history: Plot model learnable parameters history.
write_cross_validation_summary_file: Write summary data file for model cross-validation process.

Functions

`plot_model_parameters_history`(...[, ...])	Plot model learnable parameters history.
`plot_training_loss_and_lr_history`(...[, ...])	Plot model training process loss and learning rate histories.
`plot_training_loss_history`(loss_history[, ...])	Plot model training process loss history.
`plot_xy2_data`(data_xy1, data_xy2[, x_lims, ...])	Plot data in xy axes with two y axes.
`plot_xy_data`(data_xy[, data_labels, ...])	Plot data in xy axes.
`read_loss_history_from_file`(loss_record_path)	Read training process loss history from loss history record file.
`read_lr_history_from_file`(loss_record_path)	Read training learning rate history from loss history record file.
`remove_posterior_optim_state_files`(model, epoch)	Delete optimizer training epoch state files posterior to given epoch.
`save_figure`(figure, filename[, height, ...])	Save Matplotlib figure.
`save_loss_history`(model, n_max_epochs, ...)	Save training process loss history record.
`save_model_state`(model[, state_type, epoch, ...])	Save model state to file.
`save_training_state`(model, optimizer, state_type)	Save model and optimizer states at given training epoch.
`write_cross_validation_summary_file`(...)	Write summary data file for model cross-validation process.
`write_summary_file`(summary_directory[, ...])	Write summary data file with provided keyword-based parameters.
`write_training_summary_file`(device_type, ...)	Write summary data file for model training process.