A callback is a powerful tool to customize the behavior of a Keras model during
training, evaluation, or inference. Examples include keras.callbacks.TensorBoard
to visualize training progress and results with TensorBoard, or
keras.callbacks.ModelCheckpoint
to periodically save your model during training.
In this guide, you will learn what a Keras callback is, what it can do, and how you can build your own. We provide a few demos of simple callback applications to get you started.
import numpy as np import keras
All callbacks subclass the keras.callbacks.Callback
class, and
override a set of methods called at various stages of training, testing, and
predicting. Callbacks are useful to get a view on internal states and statistics of
the model during training.
You can pass a list of callbacks (as the keyword argument callbacks
) to the following
model methods:
keras.Model.fit()
keras.Model.evaluate()
keras.Model.predict()
on_(train|test|predict)_begin(self, logs=None)
Called at the beginning of fit
/evaluate
/predict
.
on_(train|test|predict)_end(self, logs=None)
Called at the end of fit
/evaluate
/predict
.
on_(train|test|predict)_batch_begin(self, batch, logs=None)
Called right before processing a batch during training/testing/predicting.
on_(train|test|predict)_batch_end(self, batch, logs=None)
Called at the end of training/testing/predicting a batch. Within this method, logs
is
a dict containing the metrics results.
on_epoch_begin(self, epoch, logs=None)
Called at the beginning of an epoch during training.
on_epoch_end(self, epoch, logs=None)
Called at the end of an epoch during training.
Let's take a look at a concrete example. To get started, let's import tensorflow and define a simple Sequential Keras model:
# Define the Keras model to add callbacks to def get_model(): model = keras.Sequential() model.add(keras.layers.Dense(1)) model.compile( optimizer=keras.optimizers.RMSprop(learning_rate=0.1), loss="mean_squared_error", metrics=["mean_absolute_error"], ) return model
Then, load the MNIST data for training and testing from Keras datasets API:
# Load example MNIST data and pre-process it (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() x_train = x_train.reshape(-1, 784).astype("float32") / 255.0 x_test = x_test.reshape(-1, 784).astype("float32") / 255.0 # Limit the data to 1000 samples x_train = x_train[:1000] y_train = y_train[:1000] x_test = x_test[:1000] y_test = y_test[:1000]
Now, define a simple custom callback that logs:
fit
/evaluate
/predict
starts & endsclass CustomCallback(keras.callbacks.Callback): def on_train_begin(self, logs=None): keys = list(logs.keys()) print("Starting training; got log keys: {}".format(keys)) def on_train_end(self, logs=None): keys = list(logs.keys()) print("Stop training; got log keys: {}".format(keys)) def on_epoch_begin(self, epoch, logs=None): keys = list(logs.keys()) print("Start epoch {} of training; got log keys: {}".format(epoch, keys)) def on_epoch_end(self, epoch, logs=None): keys = list(logs.keys()) print("End epoch {} of training; got log keys: {}".format(epoch, keys)) def on_test_begin(self, logs=None): keys = list(logs.keys()) print("Start testing; got log keys: {}".format(keys)) def on_test_end(self, logs=None): keys = list(logs.keys()) print("Stop testing; got log keys: {}".format(keys)) def on_predict_begin(self, logs=None): keys = list(logs.keys()) print("Start predicting; got log keys: {}".format(keys)) def on_predict_end(self, logs=None): keys = list(logs.keys()) print("Stop predicting; got log keys: {}".format(keys)) def on_train_batch_begin(self, batch, logs=None): keys = list(logs.keys()) print("...Training: start of batch {}; got log keys: {}".format(batch, keys)) def on_train_batch_end(self, batch, logs=None): keys = list(logs.keys()) print("...Training: end of batch {}; got log keys: {}".format(batch, keys)) def on_test_batch_begin(self, batch, logs=None): keys = list(logs.keys()) print("...Evaluating: start of batch {}; got log keys: {}".format(batch, keys)) def on_test_batch_end(self, batch, logs=None): keys = list(logs.keys()) print("...Evaluating: end of batch {}; got log keys: {}".format(batch, keys)) def on_predict_batch_begin(self, batch, logs=None): keys = list(logs.keys()) print("...Predicting: start of batch {}; got log keys: {}".format(batch, keys)) def on_predict_batch_end(self, batch, logs=None): keys = list(logs.keys()) print("...Predicting: end of batch {}; got log keys: {}".format(batch, keys))
Let's try it out:
model = get_model() model.fit( x_train, y_train, batch_size=128, epochs=1, verbose=0, validation_split=0.5, callbacks=[CustomCallback()], ) res = model.evaluate( x_test, y_test, batch_size=128, verbose=0, callbacks=[CustomCallback()] ) res = model.predict(x_test, batch_size=128, callbacks=[CustomCallback()])
logs
dictThe 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( "Up to batch {}, the average loss is {:7.2f}.".format(batch, logs["loss"]) ) def on_test_batch_end(self, batch, logs=None): print( "Up to batch {}, the average 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()], )
self.model
attributeIn 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 a few of the things you can do with self.model
in a callback:
self.model.stop_training = True
to immediately interrupt training.self.model.optimizer
),
such as self.model.optimizer.learning_rate
.model.predict()
on a few test samples at the end of each
epoch, to use as a sanity check during training.Let's see this in action in a couple of examples.
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.
keras.callbacks.EarlyStopping
provides a more complete and general implementation.
class EarlyStoppingAtMinLoss(keras.callbacks.Callback): """Stop training when the loss is at its min, i.e. the loss stops decreasing. Arguments: patience: Number of epochs to wait after min has been hit. After this number of no improvement, training stops. """ def __init__(self, patience=0): super().__init__() self.patience = patience # best_weights to store the weights at which the minimum loss occurs. self.best_weights = None def on_train_begin(self, logs=None): # The number of epoch it has waited when loss is no longer minimum. self.wait = 0 # The epoch the training stops at. self.stopped_epoch = 0 # Initialize the best as infinity. self.best = np.Inf def on_epoch_end(self, epoch, logs=None): current = logs.get("loss") if np.less(current, self.best): self.best = current self.wait = 0 # Record the best weights if current results is better (less). self.best_weights = self.model.get_weights() else: self.wait += 1 if self.wait >= self.patience: self.stopped_epoch = epoch self.model.stop_training = True print("Restoring model weights from the end of the best epoch.") self.model.set_weights(self.best_weights) def on_train_end(self, logs=None): if self.stopped_epoch > 0: print(f"Epoch {self.stopped_epoch + 1}: early stopping") model = get_model() model.fit( x_train, y_train, batch_size=64, epochs=30, verbose=0, callbacks=[LossAndErrorPrintingCallback(), EarlyStoppingAtMinLoss()], )
In this example, we show how a custom Callback can be used to dynamically change the learning rate of the optimizer during the course of training.
See callbacks.LearningRateScheduler
for a more general implementations.
class CustomLearningRateScheduler(keras.callbacks.Callback): """Learning rate scheduler which sets the learning rate according to schedule. Arguments: schedule: a function that takes an epoch index (integer, indexed from 0) and current learning rate as inputs and returns a new learning rate as output (float). """ def __init__(self, schedule): super().__init__() self.schedule = schedule def on_epoch_begin(self, epoch, logs=None): if not hasattr(self.model.optimizer, "learning_rate"): raise ValueError('Optimizer must have a "learning_rate" attribute.') # Get the current learning rate from model's optimizer. lr = self.model.optimizer.learning_rate # 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 self.model.optimizer.learning_rate = scheduled_lr print(f"\nEpoch {epoch}: Learning rate is {float(np.array(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, epochs=15, verbose=0, callbacks=[ LossAndErrorPrintingCallback(), CustomLearningRateScheduler(lr_schedule), ], )
Be sure to check out the existing Keras callbacks by reading the API docs. Applications include logging to CSV, saving the model, visualizing metrics in TensorBoard, and a lot more!
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