megnet.callbacks module¶
callbacks functions used in training process
- class ManualStop[source]¶
Bases:
keras.callbacks.Callback
Stop the training manually by putting a “STOP” file in the directory
- class ModelCheckpointMAE(filepath: str = './callback/val_mae_{epoch:05d}_{val_mae:.6f}.hdf5', monitor: str = 'val_mae', verbose: int = 0, save_best_only: bool = True, save_weights_only: bool = False, val_gen: Optional[keras.utils.data_utils.Sequence] = None, steps_per_val: Optional[int] = None, target_scaler: Optional[megnet.utils.preprocessing.Scaler] = None, period: int = 1, mode: str = 'auto')[source]¶
Bases:
keras.callbacks.Callback
Save the best MAE model with target scaler
- Parameters
filepath (string) – path to save the model file with format. For example weights.{epoch:02d}-{val_mae:.6f}.hdf5 will save the corresponding epoch and val_mae in the filename
monitor (string) – quantity to monitor, default to “val_mae”
verbose (int) – 0 for no training log, 1 for only epoch-level log and 2 for batch-level log
save_best_only (bool) – whether to save only the best model
save_weights_only (bool) – whether to save the weights only excluding model structure
val_gen (generator) – validation generator
steps_per_val (int) – steps per epoch for validation generator
target_scaler (object) – exposing inverse_transform method to scale the output
period (int) – number of epoch interval for this callback
mode – (string) choose from “min”, “max” or “auto”
- class ReduceLRUponNan(filepath: str = './callback/val_mae_{epoch:05d}_{val_mae:.6f}.hdf5', factor: float = 0.5, verbose: bool = True, patience: int = 500, monitor: str = 'val_mae', mode: str = 'auto', has_sample_weights: bool = False)[source]¶
Bases:
keras.callbacks.Callback
This callback function solves a problem that when doing regression, an nan loss may occur, or the loss suddenly shoot up. If such things happen, the model will reduce the learning rate and load the last best model during the training process. It has an extra function that patience for early stopping. This will move to indepedent callback in the future.
- Parameters
filepath (str) – filepath for saved model checkpoint, should be consistent with checkpoint callback
factor (float) – a value < 1 for scaling the learning rate
verbose (bool) – whether to show the loading event
patience (int) – number of steps that the val mae does not change. It is a criteria for early stopping
monitor (str) – target metric to monitor
mode (str) – min, max or auto
has_sample_weights (bool) – whether the data has sample weights