Validation
class Validation
Configuration for performing validation during model training.
Arguments:
data
- ARelationalData
object containing the validation data.lr_schedule
- ALrSchedule
object. Leave to None for a constant learning rate.early_stop
- AnEarlyStop
object, anEarlyStopMode
object, or a string representing anEarlyStopMode
. If None, early stop will not be performed.save_best
- A path to save the best model checkpoint. If None, the best model will not be saved.tensorboard
- A path to a TensorBoard log directory. If None, TensorBoard data will not be recorded.each
- The validation frequency.trigger
- The event triggering validation. Can be aHookEvent
object or a string representing aHookEvent
.
class EarlyStopMode
Enumeration class representing different early stop modes. Supported modes are: PRECISE, NORMAL and QUICK.
class EarlyStop
Early stop configuration.
Arguments:
delta
- The smallest variation which is considered an improvement: if the relative improvement of the monitored quantity is less than this value, the early stopping will be triggered.patience
- The number of consecutive times that the early stopping criterion must be triggered before actually failing.
class LrSchedule
Learning rate scheduler that reduces the learning rate of a given factor when the monitored quantity reaches a plateau.
Arguments:
factor
- The factor by which the learning rate will be reduced, must be between 0 and 1.patience
- The number of steps for which the condition must be triggered before actually reducing the learning rate.threshold
- The threshold for measuring the new optimum, to only focus on significant changes.cooldown
- The number of steps to wait before resuming normal operation after the learning rate has been reduced.lr_min_ratio
- The ratio to compute the minimal learning rate, i.e. lr * self.lr_min_ratio. The learning rate will never go below such value.