Keras ReduceLROnPlateau - How to check if it is applied - tensorflow

Question

Please advise how to confirm if ReduceLROnPlateau has been actually applied and what learning rates have been applied at each epoch.
The patience of ReduceLROnPlateau is set to 2 and monitor var_loss.
self._history = self.model.fit(
self.X.shuffle(1000).batch(self.batch_size).prefetch(1),
epochs=self.num_epochs,
batch_size=self.batch_size,
validation_data=self.V.shuffle(1000).batch(self.batch_size).prefetch(1),
callbacks=[
EaryStoppingCallback(patience=self.early_stop_patience),
ReduceLRCallback(patience=self.reduce_lr_patience), # <---- set to 2
TensorBoardCallback(self.log_directory)
]
)
class ReduceLRCallback(tf.keras.callbacks.ReduceLROnPlateau):
"""Reduce learning rate when a metric has stopped improving.
See https://keras.io/api/callbacks/reduce_lr_on_plateau/
"""
def __init__(self, patience=3):
assert patience > 0
super().__init__(
monitor="val_loss",
factor=0.3,
patience=patience,
)
The training val_loss have increased more than twice but have not seen any information if ReduceLROnPlateau has been applied.
_________________________________________________________________
Epoch 1/20
3990/3990 [==============================] - 860s 214ms/step - loss: 0.1705 - accuracy: 0.9386 - val_loss: 0.1626 - val_accuracy: 0.9456
Epoch 2/20
3990/3990 [==============================] - 847s 212ms/step - loss: 0.1618 - accuracy: 0.9412 - val_loss: 0.1433 - val_accuracy: 0.9456
Epoch 3/20
3990/3990 [==============================] - 846s 212ms/step - loss: 0.1593 - accuracy: 0.9425 - val_loss: 0.1478 - val_accuracy: 0.9438
Epoch 4/20
3990/3990 [==============================] - 846s 212ms/step - loss: 0.1567 - accuracy: 0.9427 - val_loss: 0.1428 - val_accuracy: 0.9468
Epoch 5/20
3990/3990 [==============================] - 846s 212ms/step - loss: 0.1558 - accuracy: 0.9425 - val_loss: 0.1502 - val_accuracy: 0.9425
Epoch 6/20
3990/3990 [==============================] - 843s 211ms/step - loss: 0.1554 - accuracy: 0.9433 - val_loss: 0.1453 - val_accuracy: 0.9456
Epoch 7/20
3990/3990 [==============================] - 843s 211ms/step - loss: 0.1482 - accuracy: 0.9454 - val_loss: 0.1362 - val_accuracy: 0.9477
Epoch 8/20
3990/3990 [==============================] - 843s 211ms/step - loss: 0.1475 - accuracy: 0.9449 - val_loss: 0.1373 - val_accuracy: 0.9471
Epoch 9/20
3990/3990 [==============================] - 845s 212ms/step - loss: 0.1468 - accuracy: 0.9460 - val_loss: 0.1362 - val_accuracy: 0.9485
Epoch 10/20
3990/3990 [==============================] - 843s 211ms/step - loss: 0.1448 - accuracy: 0.9462 - val_loss: 0.1344 - val_accuracy: 0.9489
Epoch 11/20
3990/3990 [==============================] - 846s 212ms/step - loss: 0.1447 - accuracy: 0.9458 - val_loss: 0.1346 - val_accuracy: 0.9483
Epoch 12/20
3990/3990 [==============================] - 843s 211ms/step - loss: 0.1444 - accuracy: 0.9460 - val_loss: 0.1342 - val_accuracy: 0.9483

Answer 1

Marco Cerliani's answer will probably solve it for you. That gives console output so you can verify the LR was actually reduced.
If you needed to check the model's learning rate in code, you can use
tf.keras.backend.get_value(model.optimizer.lr)