Usage of callbacks

A callback is a set of functions to be applied at given stages of the training procedure. You can use callbacks 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 .fit() method of the Sequential model. The relevant methods of the callbacks will then be called at each stage of the training.

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CallbackList

keras.callbacks.CallbackList(callbacks=[], queue_length=10)

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Callback

keras.callbacks.Callback()

Abstract base class used to build new callbacks.

Properties

  • params: dict. Training parameters (eg. verbosity, batch size, number of epochs...).
  • model: instance of keras.models.Model. Reference of the model being trained.

The logs dictionary that callback methods take as argument will contain keys for quantities relevant to the current batch or epoch.

Currently, the .fit() method of the Sequential model class will include the following quantities in the logs that it passes to its callbacks:

  • on_epoch_end: logs include acc and loss, and optionally include val_loss (if validation is enabled in fit), and val_acc (if validation and accuracy monitoring are enabled).
  • on_batch_begin: logs include size, the number of samples in the current batch.
  • on_batch_end: logs include loss, and optionally acc (if accuracy monitoring is enabled).

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BaseLogger

keras.callbacks.BaseLogger()

Callback that accumulates epoch averages of the metrics being monitored.

This callback is automatically applied to every Keras model.

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ProgbarLogger

keras.callbacks.ProgbarLogger()

Callback that prints metrics to stdout.

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History

keras.callbacks.History()

Callback that records events into a History object.

This callback is automatically applied to every Keras model. The History object gets returned by the fit method of models.

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ModelCheckpoint

keras.callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=False, mode='auto')

Save the model after every epoch.

filepath can contain named formatting options, which will be filled the value of epoch and keys in logs (passed in on_epoch_end).

For example: if filepath is weights.{epoch:02d}-{val_loss:.2f}.hdf5, then multiple files will be save with the epoch number and the validation loss.

Arguments

  • filepath: string, path to save the model file.
  • monitor: quantity to monitor.
  • verbose: verbosity mode, 0 or 1.
  • save_best_only: if save_best_only=True, the latest best model according to the validation loss will not be overwritten.
  • mode: one of {auto, min, max}. If save_best_only=True, the decision to overwrite the current save file is made based on either the maximization or the minization of the monitored. For val_acc, this should be max, for val_loss this should be min, etc. In auto mode, the direction is automatically inferred from the name of the monitored quantity.

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EarlyStopping

keras.callbacks.EarlyStopping(monitor='val_loss', patience=0, verbose=0, mode='auto')

Stop training when a monitored quantity has stopped improving.

Arguments

  • monitor: quantity to be monitored.
  • patience: number of epochs with no improvement after which training will be stopped.
  • verbose: verbosity mode.
  • mode: one of {auto, min, max}. In 'min' mode, training will stop when the quantity monitored has stopped decreasing; in 'max' mode it will stop when the quantity monitored has stopped increasing.

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RemoteMonitor

keras.callbacks.RemoteMonitor(root='http://localhost:9000', path='/publish/epoch/end/', field='data')

Callback used to stream events to a server.

Requires the requests library.

Arguments

  • root: root url to which the events will be sent (at the end of every epoch). Events are sent to root + '/publish/epoch/end/' by default. Calls are HTTP POST, with a data argument which is a JSON-encoded dictionary of event data.

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LearningRateScheduler

keras.callbacks.LearningRateScheduler(schedule)

Learning rate scheduler.

Arguments

  • schedule: a function that takes an epoch index as input (integer, indexed from 0) and returns a new learning rate as output (float).

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TensorBoard

keras.callbacks.TensorBoard(log_dir='./logs', histogram_freq=0, write_graph=True)

Tensorboard basic visualizations.

This callback writes a log for TensorBoard, which allows you to visualize dynamic graphs of your training and test metrics, as well as activation histograms for the different layers in your model.

TensorBoard is a visualization tool provided with TensorFlow.

If you have installed TensorFlow with pip, you should be able to launch TensorBoard from the command line:

tensorboard --logdir=/full_path_to_your_logs

You can find more information about TensorBoard - __here.

Arguments

  • log_dir: the path of the directory where to save the log files to be parsed by Tensorboard
  • histogram_freq: frequency (in epochs) at which to compute activation histograms for the layers of the model. If set to 0, histograms won't be computed.
  • write_graph: whether to visualize the graph in Tensorboard. The log file can become quite large when write_graph is set to True.

Create a callback

You can create a custom callback by extending the base class keras.callbacks.Callback. A callback has access to its associated model through the class property self.model.

Here's a simple example saving a list of losses over each batch during training:

class LossHistory(keras.callbacks.Callback):
    def on_train_begin(self, logs={}):
        self.losses = []

    def on_batch_end(self, batch, logs={}):
        self.losses.append(logs.get('loss'))

Example: recording loss history

class LossHistory(keras.callbacks.Callback):
    def on_train_begin(self, logs={}):
        self.losses = []

    def on_batch_end(self, batch, logs={}):
        self.losses.append(logs.get('loss'))

model = Sequential()
model.add(Dense(10, input_dim=784, init='uniform'))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

history = LossHistory()
model.fit(X_train, Y_train, batch_size=128, nb_epoch=20, verbose=0, callbacks=[history])

print history.losses
# outputs
'''
[0.66047596406559383, 0.3547245744908703, ..., 0.25953155204159617, 0.25901699725311789]
'''

Example: model checkpoints

from keras.callbacks import ModelCheckpoint

model = Sequential()
model.add(Dense(10, input_dim=784, init='uniform'))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

'''
saves the model weights after each epoch if the validation loss decreased
'''
checkpointer = ModelCheckpoint(filepath="/tmp/weights.hdf5", verbose=1, save_best_only=True)
model.fit(X_train, Y_train, batch_size=128, nb_epoch=20, verbose=0, validation_data=(X_test, Y_test), callbacks=[checkpointer])