Datasets:
HenryAI
/
KerasBERTv1-Data

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...Predicting: start of batch 2; got log keys: []
...Predicting: end of batch 2; got log keys: ['outputs']
...Predicting: start of batch 3; got log keys: []
...Predicting: end of batch 3; got log keys: ['outputs']
...Predicting: start of batch 4; got log keys: []
...Predicting: end of batch 4; got log keys: ['outputs']
...Predicting: start of batch 5; got log keys: []
...Predicting: end of batch 5; got log keys: ['outputs']
...Predicting: start of batch 6; got log keys: []
...Predicting: end of batch 6; got log keys: ['outputs']
...Predicting: start of batch 7; got log keys: []
...Predicting: end of batch 7; got log keys: ['outputs']
Stop predicting; got log keys: []
Usage of logs dict
The logs dict contains the loss value, and all the metrics at the end of a batch or epoch. Example includes the loss and mean absolute error.
class LossAndErrorPrintingCallback(keras.callbacks.Callback):
def on_train_batch_end(self, batch, logs=None):
print("For batch {}, loss is {:7.2f}.".format(batch, logs["loss"]))
def on_test_batch_end(self, batch, logs=None):
print("For batch {}, loss is {:7.2f}.".format(batch, logs["loss"]))
def on_epoch_end(self, epoch, logs=None):
print(
"The average loss for epoch {} is {:7.2f} "
"and mean absolute error is {:7.2f}.".format(
epoch, logs["loss"], logs["mean_absolute_error"]
)
)
model = get_model()
model.fit(
x_train,
y_train,
batch_size=128,
epochs=2,
verbose=0,
callbacks=[LossAndErrorPrintingCallback()],
)
res = model.evaluate(
x_test,
y_test,
batch_size=128,
verbose=0,
callbacks=[LossAndErrorPrintingCallback()],
)
For batch 0, loss is 32.45.
For batch 1, loss is 393.79.
For batch 2, loss is 272.00.
For batch 3, loss is 206.95.
For batch 4, loss is 167.29.
For batch 5, loss is 140.41.
For batch 6, loss is 121.19.
For batch 7, loss is 109.21.
The average loss for epoch 0 is 109.21 and mean absolute error is 5.83.
For batch 0, loss is 5.94.
For batch 1, loss is 5.73.
For batch 2, loss is 5.50.
For batch 3, loss is 5.38.
For batch 4, loss is 5.16.
For batch 5, loss is 5.19.
For batch 6, loss is 5.64.
For batch 7, loss is 7.05.
The average loss for epoch 1 is 7.05 and mean absolute error is 2.14.
For batch 0, loss is 40.89.
For batch 1, loss is 42.12.
For batch 2, loss is 41.42.
For batch 3, loss is 42.10.
For batch 4, loss is 42.05.
For batch 5, loss is 42.91.
For batch 6, loss is 43.05.
For batch 7, loss is 42.94.
Usage of self.model attribute
In addition to receiving log information when one of their methods is called, callbacks have access to the model associated with the current round of training/evaluation/inference: self.model.
Here are of few of the things you can do with self.model in a callback:
Set self.model.stop_training = True to immediately interrupt training.
Mutate hyperparameters of the optimizer (available as self.model.optimizer), such as self.model.optimizer.learning_rate.
Save the model at period intervals.
Record the output of model.predict() on a few test samples at the end of each epoch, to use as a sanity check during training.
Extract visualizations of intermediate features at the end of each epoch, to monitor what the model is learning over time.
etc.
Let's see this in action in a couple of examples.
Examples of Keras callback applications
Early stopping at minimum loss
This first example shows the creation of a Callback that stops training when the minimum of loss has been reached, by setting the attribute self.model.stop_training (boolean). Optionally, you can provide an argument patience to specify how many epochs we should wait before stopping after having reached a local minimum.
tf.keras.callbacks.EarlyStopping provides a more complete and general implementation.
import numpy as np
class EarlyStoppingAtMinLoss(keras.callbacks.Callback):
"""Stop training when the loss is at its min, i.e. the loss stops decreasing.