Datasets:
HenryAI
/
KerasBERTv1-Data

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# Call schedule function to get the scheduled learning rate.
scheduled_lr = self.schedule(epoch, lr)
# Set the value back to the optimizer before this epoch starts
tf.keras.backend.set_value(self.model.optimizer.lr, scheduled_lr)
print("\nEpoch %05d: Learning rate is %6.4f." % (epoch, scheduled_lr))
LR_SCHEDULE = [
# (epoch to start, learning rate) tuples
(3, 0.05),
(6, 0.01),
(9, 0.005),
(12, 0.001),
]
def lr_schedule(epoch, lr):
"""Helper function to retrieve the scheduled learning rate based on epoch."""
if epoch < LR_SCHEDULE[0][0] or epoch > LR_SCHEDULE[-1][0]:
return lr
for i in range(len(LR_SCHEDULE)):
if epoch == LR_SCHEDULE[i][0]:
return LR_SCHEDULE[i][1]
return lr
model = get_model()
model.fit(
x_train,
y_train,
batch_size=64,
steps_per_epoch=5,
epochs=15,
verbose=0,
callbacks=[
LossAndErrorPrintingCallback(),
CustomLearningRateScheduler(lr_schedule),
],
)
Epoch 00000: Learning rate is 0.1000.
For batch 0, loss is 32.53.
For batch 1, loss is 430.35.
For batch 2, loss is 294.47.
For batch 3, loss is 223.69.
For batch 4, loss is 180.61.
The average loss for epoch 0 is 180.61 and mean absolute error is 8.20.
Epoch 00001: Learning rate is 0.1000.
For batch 0, loss is 6.72.
For batch 1, loss is 5.57.
For batch 2, loss is 5.33.
For batch 3, loss is 5.35.
For batch 4, loss is 5.53.
The average loss for epoch 1 is 5.53 and mean absolute error is 1.92.
Epoch 00002: Learning rate is 0.1000.
For batch 0, loss is 5.22.
For batch 1, loss is 5.19.
For batch 2, loss is 5.51.
For batch 3, loss is 5.80.
For batch 4, loss is 5.69.
The average loss for epoch 2 is 5.69 and mean absolute error is 1.99.
Epoch 00003: Learning rate is 0.0500.
For batch 0, loss is 6.21.
For batch 1, loss is 4.85.
For batch 2, loss is 4.90.
For batch 3, loss is 4.66.
For batch 4, loss is 4.54.
The average loss for epoch 3 is 4.54 and mean absolute error is 1.69.
Epoch 00004: Learning rate is 0.0500.
For batch 0, loss is 3.62.
For batch 1, loss is 3.58.
For batch 2, loss is 3.92.
For batch 3, loss is 3.73.
For batch 4, loss is 3.65.
The average loss for epoch 4 is 3.65 and mean absolute error is 1.57.
Epoch 00005: Learning rate is 0.0500.
For batch 0, loss is 4.42.
For batch 1, loss is 4.95.
For batch 2, loss is 5.83.
For batch 3, loss is 6.36.
For batch 4, loss is 6.62.
The average loss for epoch 5 is 6.62 and mean absolute error is 2.09.
Epoch 00006: Learning rate is 0.0100.
For batch 0, loss is 8.74.
For batch 1, loss is 7.34.
For batch 2, loss is 5.55.
For batch 3, loss is 4.98.
For batch 4, loss is 4.48.
The average loss for epoch 6 is 4.48 and mean absolute error is 1.65.
Epoch 00007: Learning rate is 0.0100.
For batch 0, loss is 4.30.
For batch 1, loss is 4.01.
For batch 2, loss is 3.97.
For batch 3, loss is 3.68.
For batch 4, loss is 3.76.
The average loss for epoch 7 is 3.76 and mean absolute error is 1.51.
Epoch 00008: Learning rate is 0.0100.
For batch 0, loss is 3.41.
For batch 1, loss is 3.74.
For batch 2, loss is 3.51.
For batch 3, loss is 3.52.