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 or Model classes. The relevant methods of the callbacks will then be called at each stage of the training.

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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 metrics.

This callback is automatically applied to every Keras model.

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TerminateOnNaN

keras.callbacks.TerminateOnNaN()

Callback that terminates training when a NaN loss is encountered.

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ProgbarLogger

keras.callbacks.ProgbarLogger(count_mode='samples')

Callback that prints metrics to stdout.

Arguments

  • count_mode: One of "steps" or "samples". Whether the progress bar should count samples seens or steps (batches) seen.

Raises

  • ValueError: In case of invalid count_mode.

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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, save_weights_only=False, mode='auto', period=1)

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 the model checkpoints will be saved with the epoch number and the validation loss in the filename.

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 quantity monitored 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 minimization of the monitored quantity. 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.
  • save_weights_only: if True, then only the model's weights will be saved (model.save_weights(filepath)), else the full model is saved (model.save(filepath)).
  • period: Interval (number of epochs) between checkpoints.

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EarlyStopping

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

Stop training when a monitored quantity has stopped improving.

Arguments

  • monitor: quantity to be monitored.
  • min_delta: minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement.
  • 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; in auto mode, the direction is automatically inferred from the name of the monitored quantity.

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RemoteMonitor

keras.callbacks.RemoteMonitor(root='http://localhost:9000', path='/publish/epoch/end/', field='data', headers=None)

Callback used to stream events to a server.

Requires the requests library. 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.

Arguments

  • root: String; root url of the target server.
  • path: String; path relative to root to which the events will be sent.
  • field: String; JSON field under which the data will be stored.
  • headers: Dictionary; optional custom HTTP headers.

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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, batch_size=32, write_graph=True, write_grads=False, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None)

Tensorboard basic visualizations.

TensorBoard is a visualization tool provided with TensorFlow.

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.

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

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 and weight histograms for the layers of the model. If set to 0, histograms won't be computed. Validation data (or split) must be specified for histogram visualizations.
  • write_graph: whether to visualize the graph in TensorBoard. The log file can become quite large when write_graph is set to True.
  • write_grads: whether to visualize gradient histograms in TensorBoard. histogram_freq must be greater than 0.
  • batch_size: size of batch of inputs to feed to the network for histograms computation.
  • write_images: whether to write model weights to visualize as image in TensorBoard.
  • embeddings_freq: frequency (in epochs) at which selected embedding layers will be saved.
  • embeddings_layer_names: a list of names of layers to keep eye on. If None or empty list all the embedding layer will be watched.
  • embeddings_metadata: a dictionary which maps layer name to a file name in which metadata for this embedding layer is saved. See the details about metadata files format. In case if the same metadata file is used for all embedding layers, string can be passed.

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ReduceLROnPlateau

keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=0, mode='auto', epsilon=0.0001, cooldown=0, min_lr=0)

Reduce learning rate when a metric has stopped improving.

Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. This callback monitors a quantity and if no improvement is seen for a 'patience' number of epochs, the learning rate is reduced.

Example

reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,
              patience=5, min_lr=0.001)
model.fit(X_train, Y_train, callbacks=[reduce_lr])

Arguments

  • monitor: quantity to be monitored.
  • factor: factor by which the learning rate will be reduced. new_lr = lr * factor
  • patience: number of epochs with no improvement after which learning rate will be reduced.
  • verbose: int. 0: quiet, 1: update messages.
  • mode: one of {auto, min, max}. In min mode, lr will be reduced when the quantity monitored has stopped decreasing; in max mode it will be reduced when the quantity monitored has stopped increasing; in auto mode, the direction is automatically inferred from the name of the monitored quantity.
  • epsilon: threshold for measuring the new optimum, to only focus on significant changes.
  • cooldown: number of epochs to wait before resuming normal operation after lr has been reduced.
  • min_lr: lower bound on the learning rate.

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CSVLogger

keras.callbacks.CSVLogger(filename, separator=',', append=False)

Callback that streams epoch results to a csv file.

Supports all values that can be represented as a string, including 1D iterables such as np.ndarray.

Example

csv_logger = CSVLogger('training.log')
model.fit(X_train, Y_train, callbacks=[csv_logger])

Arguments

  • filename: filename of the csv file, e.g. 'run/log.csv'.
  • separator: string used to separate elements in the csv file.
  • append: True: append if file exists (useful for continuing training). False: overwrite existing file,

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LambdaCallback

keras.callbacks.LambdaCallback(on_epoch_begin=None, on_epoch_end=None, on_batch_begin=None, on_batch_end=None, on_train_begin=None, on_train_end=None)

Callback for creating simple, custom callbacks on-the-fly.

This callback is constructed with anonymous functions that will be called at the appropriate time. Note that the callbacks expects positional arguments, as:

  • on_epoch_begin and on_epoch_end expect two positional arguments: epoch, logs
  • on_batch_begin and on_batch_end expect two positional arguments: batch, logs
  • on_train_begin and on_train_end expect one positional argument: logs

Arguments

  • on_epoch_begin: called at the beginning of every epoch.
  • on_epoch_end: called at the end of every epoch.
  • on_batch_begin: called at the beginning of every batch.
  • on_batch_end: called at the end of every batch.
  • on_train_begin: called at the beginning of model training.
  • on_train_end: called at the end of model training.

Example

# Print the batch number at the beginning of every batch.
batch_print_callback = LambdaCallback(
    on_batch_begin=lambda batch,logs: print(batch))

# Stream the epoch loss to a file in JSON format. The file content
# is not well-formed JSON but rather has a JSON object per line.
import json
json_log = open('loss_log.json', mode='wt', buffering=1)
json_logging_callback = LambdaCallback(
    on_epoch_end=lambda epoch, logs: json_log.write(
    json.dumps({'epoch': epoch, 'loss': logs['loss']}) + '\n'),
    on_train_end=lambda logs: json_log.close()
)

# Terminate some processes after having finished model training.
processes = ...
cleanup_callback = LambdaCallback(
    on_train_end=lambda logs: [
    p.terminate() for p in processes if p.is_alive()])

model.fit(...,
      callbacks=[batch_print_callback,
         json_logging_callback,
         cleanup_callback])

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, kernel_initializer='uniform'))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

history = LossHistory()
model.fit(x_train, y_train, batch_size=128, epochs=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, kernel_initializer='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, epochs=20, verbose=0, validation_data=(X_test, Y_test), callbacks=[checkpointer])