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Disable early stopping and LR reduction on plateau by default

This commit is contained in:
Reuben Morais 2020-02-18 16:14:33 +01:00
parent 6e12b7caed
commit 78e8dfdf38
1 changed files with 2 additions and 2 deletions
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4
util/flags.py
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@ -139,13 +139,13 @@ def create_flags():
# Early Stopping
f.DEFINE_boolean('early_stop', True, 'Enable early stopping mechanism over validation dataset. If validation is not being run, early stopping is disabled.')
f.DEFINE_boolean('early_stop', False, 'Enable early stopping mechanism over validation dataset. If validation is not being run, early stopping is disabled.')
f.DEFINE_integer('es_epochs', 25, 'Number of epochs with no improvement after which training will be stopped. Loss is not stored in the checkpoint so when checkpoint is revived it starts the loss calculation from start at that point')
f.DEFINE_float('es_min_delta', 0.05, 'Minimum change in loss to qualify as an improvement. This value will also be used in Reduce learning rate on plateau')
# Reduce learning rate on plateau
f.DEFINE_boolean('reduce_lr_on_plateau', True, 'Enable reducing the learning rate if a plateau is reached. This is the case if the validation loss did not improve for some epochs.')
f.DEFINE_boolean('reduce_lr_on_plateau', False, 'Enable reducing the learning rate if a plateau is reached. This is the case if the validation loss did not improve for some epochs.')
f.DEFINE_integer('plateau_epochs', 10, 'Number of epochs to consider for RLROP. Has to be smaller than es_epochs from early stopping')
f.DEFINE_float('plateau_reduction', 0.1, 'Multiplicative factor to apply to the current learning rate if a plateau has occurred.')